diff --git a/ratings-ui-demo/package-lock.json b/ratings-ui-demo/package-lock.json
index 0e53293124362acba10c0d2629abea6378c72647..93b70470d682d1a818404dfbba11b992c569d269 100644
--- a/ratings-ui-demo/package-lock.json
+++ b/ratings-ui-demo/package-lock.json
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@@ -15048,8 +13202,6 @@
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                 {
@@ -15079,14 +13231,10 @@
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-            "resolved": "https://registry.npmjs.org/update-diff/-/update-diff-1.1.0.tgz",
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             "dev": true,
             "license": "BSD-2-Clause",
             "dependencies": {
@@ -15095,8 +13243,6 @@
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         "node_modules/use-callback-ref": {
             "version": "1.3.3",
-            "resolved": "https://registry.npmjs.org/use-callback-ref/-/use-callback-ref-1.3.3.tgz",
-            "integrity": "sha512-jQL3lRnocaFtu3V00JToYz/4QkNWswxijDaCVNZRiRTO3HQDLsdu1ZtmIUvV4yPp+rvWm5j0y0TG/S61cuijTg==",
             "license": "MIT",
             "dependencies": {
                 "tslib": "^2.0.0"
@@ -15116,8 +13262,6 @@
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-            "resolved": "https://registry.npmjs.org/use-sidecar/-/use-sidecar-1.1.3.tgz",
-            "integrity": "sha512-Fedw0aZvkhynoPYlA5WXrMCAMm+nSWdZt6lzJQ7Ok8S6Q+VsHmHpRWndVRJ8Be0ZbkfPc5LRYH+5XrzXcEeLRQ==",
             "license": "MIT",
             "dependencies": {
                 "detect-node-es": "^1.1.0",
@@ -15138,8 +13282,6 @@
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-            "resolved": "https://registry.npmjs.org/use-sync-external-store/-/use-sync-external-store-1.6.0.tgz",
-            "integrity": "sha512-Pp6GSwGP/NrPIrxVFAIkOQeyw8lFenOHijQWkUTrDvrF4ALqylP2C/KCkeS9dpUM3KvYRQhna5vt7IL95+ZQ9w==",
             "license": "MIT",
             "peerDependencies": {
                 "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0"
@@ -15147,14 +13289,10 @@
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-            "resolved": "https://registry.npmjs.org/util-deprecate/-/util-deprecate-1.0.2.tgz",
-            "integrity": "sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw==",
             "license": "MIT"
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-            "resolved": "https://registry.npmjs.org/uuid/-/uuid-8.3.2.tgz",
-            "integrity": "sha512-+NYs2QeMWy+GWFOEm9xnn6HCDp0l7QBD7ml8zLUmJ+93Q5NF0NocErnwkTkXVFNiX3/fpC6afS8Dhb/gz7R7eg==",
             "dev": true,
             "license": "MIT",
             "bin": {
@@ -15163,8 +13301,6 @@
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         "node_modules/uvu": {
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-            "resolved": "https://registry.npmjs.org/uvu/-/uvu-0.5.6.tgz",
-            "integrity": "sha512-+g8ENReyr8YsOc6fv/NVJs2vFdHBnBNdfE49rshrTzDWOlUx4Gq7KOS2GD8eqhy2j+Ejq29+SbKH8yjkAqXqoA==",
             "license": "MIT",
             "dependencies": {
                 "dequal": "^2.0.0",
@@ -15181,8 +13317,6 @@
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         "node_modules/verror": {
             "version": "1.10.0",
-            "resolved": "https://registry.npmjs.org/verror/-/verror-1.10.0.tgz",
-            "integrity": "sha512-ZZKSmDAEFOijERBLkmYfJ+vmk3w+7hOLYDNkRCuRuMJGEmqYNCNLyBBFwWKVMhfwaEF3WOd0Zlw86U/WC/+nYw==",
             "dev": true,
             "engines": [
                 "node >=0.6.0"
@@ -15196,15 +13330,11 @@
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         "node_modules/verror/node_modules/core-util-is": {
             "version": "1.0.2",
-            "resolved": "https://registry.npmjs.org/core-util-is/-/core-util-is-1.0.2.tgz",
-            "integrity": "sha512-3lqz5YjWTYnW6dlDa5TLaTCcShfar1e40rmcJVwCBJC6mWlFuj0eCHIElmG1g5kyuJ/GD+8Wn4FFCcz4gJPfaQ==",
             "dev": true,
             "license": "MIT"
         },
         "node_modules/vfile": {
             "version": "6.0.3",
-            "resolved": "https://registry.npmjs.org/vfile/-/vfile-6.0.3.tgz",
-            "integrity": "sha512-KzIbH/9tXat2u30jf+smMwFCsno4wHVdNmzFyL+T/L3UGqqk6JKfVqOFOZEpZSHADH1k40ab6NUIXZq422ov3Q==",
             "license": "MIT",
             "dependencies": {
                 "@types/unist": "^3.0.0",
@@ -15217,8 +13347,6 @@
         },
         "node_modules/vfile-matter": {
             "version": "5.0.1",
-            "resolved": "https://registry.npmjs.org/vfile-matter/-/vfile-matter-5.0.1.tgz",
-            "integrity": "sha512-o6roP82AiX0XfkyTHyRCMXgHfltUNlXSEqCIS80f+mbAyiQBE2fxtDVMtseyytGx75sihiJFo/zR6r/4LTs2Cw==",
             "license": "MIT",
             "dependencies": {
                 "vfile": "^6.0.0",
@@ -15231,8 +13359,6 @@
         },
         "node_modules/vfile-matter/node_modules/yaml": {
             "version": "2.8.2",
-            "resolved": "https://registry.npmjs.org/yaml/-/yaml-2.8.2.tgz",
-            "integrity": "sha512-mplynKqc1C2hTVYxd0PU2xQAc22TI1vShAYGksCCfxbn/dFwnHTNi1bvYsBTkhdUNtGIf5xNOg938rrSSYvS9A==",
             "license": "ISC",
             "bin": {
                 "yaml": "bin.mjs"
@@ -15246,8 +13372,6 @@
         },
         "node_modules/vfile-message": {
             "version": "4.0.3",
-            "resolved": "https://registry.npmjs.org/vfile-message/-/vfile-message-4.0.3.tgz",
-            "integrity": "sha512-QTHzsGd1EhbZs4AsQ20JX1rC3cOlt/IWJruk893DfLRr57lcnOeMaWG4K0JrRta4mIJZKth2Au3mM3u03/JWKw==",
             "license": "MIT",
             "dependencies": {
                 "@types/unist": "^3.0.0",
@@ -15260,8 +13384,6 @@
         },
         "node_modules/vt-pbf": {
             "version": "3.1.3",
-            "resolved": "https://registry.npmjs.org/vt-pbf/-/vt-pbf-3.1.3.tgz",
-            "integrity": "sha512-2LzDFzt0mZKZ9IpVF2r69G9bXaP2Q2sArJCmcCgvfTdCCZzSyz4aCLoQyUilu37Ll56tCblIZrXFIjNUpGIlmA==",
             "license": "MIT",
             "dependencies": {
                 "@mapbox/point-geometry": "0.1.0",
@@ -15271,20 +13393,14 @@
         },
         "node_modules/w3c-keyname": {
             "version": "2.2.8",
-            "resolved": "https://registry.npmjs.org/w3c-keyname/-/w3c-keyname-2.2.8.tgz",
-            "integrity": "sha512-dpojBhNsCNN7T82Tm7k26A6G9ML3NkhDsnw9n/eoxSRlVBB4CEtIQ/KTCLI2Fwf3ataSXRhYFkQi3SlnFwPvPQ==",
             "license": "MIT"
         },
         "node_modules/weak-map": {
             "version": "1.0.8",
-            "resolved": "https://registry.npmjs.org/weak-map/-/weak-map-1.0.8.tgz",
-            "integrity": "sha512-lNR9aAefbGPpHO7AEnY0hCFjz1eTkWCXYvkTRrTHs9qv8zJp+SkVYpzfLIFXQQiG3tVvbNFQgVg2bQS8YGgxyw==",
             "license": "Apache-2.0"
         },
         "node_modules/webgl-context": {
             "version": "2.2.0",
-            "resolved": "https://registry.npmjs.org/webgl-context/-/webgl-context-2.2.0.tgz",
-            "integrity": "sha512-q/fGIivtqTT7PEoF07axFIlHNk/XCPaYpq64btnepopSWvKNFkoORlQYgqDigBIuGA1ExnFd/GnSUnBNEPQY7Q==",
             "license": "MIT",
             "dependencies": {
                 "get-canvas-context": "^1.0.1"
@@ -15292,8 +13408,6 @@
         },
         "node_modules/which": {
             "version": "2.0.2",
-            "resolved": "https://registry.npmjs.org/which/-/which-2.0.2.tgz",
-            "integrity": "sha512-BLI3Tl1TW3Pvl70l3yq3Y64i+awpwXqsGBYWkkqMtnbXgrMD+yj7rhW0kuEDxzJaYXGjEW5ogapKNMEKNMjibA==",
             "dev": true,
             "license": "ISC",
             "dependencies": {
@@ -15308,8 +13422,6 @@
         },
         "node_modules/which-boxed-primitive": {
             "version": "1.1.1",
-            "resolved": "https://registry.npmjs.org/which-boxed-primitive/-/which-boxed-primitive-1.1.1.tgz",
-            "integrity": "sha512-TbX3mj8n0odCBFVlY8AxkqcHASw3L60jIuF8jFP78az3C2YhmGvqbHBpAjTRH2/xqYunrJ9g1jSyjCjpoWzIAA==",
             "dev": true,
             "license": "MIT",
             "dependencies": {
@@ -15328,8 +13440,6 @@
         },
         "node_modules/which-builtin-type": {
             "version": "1.2.1",
-            "resolved": "https://registry.npmjs.org/which-builtin-type/-/which-builtin-type-1.2.1.tgz",
-            "integrity": "sha512-6iBczoX+kDQ7a3+YJBnh3T+KZRxM/iYNPXicqk66/Qfm1b93iu+yOImkg0zHbj5LNOcNv1TEADiZ0xa34B4q6Q==",
             "dev": true,
             "license": "MIT",
             "dependencies": {
@@ -15356,8 +13466,6 @@
         },
         "node_modules/which-collection": {
             "version": "1.0.2",
-            "resolved": "https://registry.npmjs.org/which-collection/-/which-collection-1.0.2.tgz",
-            "integrity": "sha512-K4jVyjnBdgvc86Y6BkaLZEN933SwYOuBFkdmBu9ZfkcAbdVbpITnDmjvZ/aQjRXQrv5EPkTnD1s39GiiqbngCw==",
             "dev": true,
             "license": "MIT",
             "dependencies": {
@@ -15375,8 +13483,6 @@
         },
         "node_modules/which-typed-array": {
             "version": "1.1.19",
-            "resolved": "https://registry.npmjs.org/which-typed-array/-/which-typed-array-1.1.19.tgz",
-            "integrity": "sha512-rEvr90Bck4WZt9HHFC4DJMsjvu7x+r6bImz0/BrbWb7A2djJ8hnZMrWnHo9F8ssv0OMErasDhftrfROTyqSDrw==",
             "dev": true,
             "license": "MIT",
             "dependencies": {
@@ -15397,8 +13503,6 @@
         },
         "node_modules/word-wrap": {
             "version": "1.2.5",
-            "resolved": "https://registry.npmjs.org/word-wrap/-/word-wrap-1.2.5.tgz",
-            "integrity": "sha512-BN22B5eaMMI9UMtjrGd5g5eCYPpCPDUy0FJXbYsaT5zYxjFOckS53SQDE3pWkVoWpHXVb3BrYcEN4Twa55B5cA==",
             "dev": true,
             "license": "MIT",
             "engines": {
@@ -15407,8 +13511,6 @@
         },
         "node_modules/world-calendars": {
             "version": "1.0.4",
-            "resolved": "https://registry.npmjs.org/world-calendars/-/world-calendars-1.0.4.tgz",
-            "integrity": "sha512-VGRnLJS+xJmGDPodgJRnGIDwGu0s+Cr9V2HB3EzlDZ5n0qb8h5SJtGUEkjrphZYAglEiXZ6kiXdmk0H/h/uu/w==",
             "license": "MIT",
             "dependencies": {
                 "object-assign": "^4.1.0"
@@ -15416,8 +13518,6 @@
         },
         "node_modules/wrap-ansi": {
             "version": "7.0.0",
-            "resolved": "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-7.0.0.tgz",
-            "integrity": "sha512-YVGIj2kamLSTxw6NsZjoBxfSwsn0ycdesmc4p+Q21c5zPuZ1pl+NfxVdxPtdHvmNVOQ6XSYG4AUtyt/Fi7D16Q==",
             "dev": true,
             "license": "MIT",
             "dependencies": {
@@ -15434,14 +13534,10 @@
         },
         "node_modules/wrappy": {
             "version": "1.0.2",
-            "resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz",
-            "integrity": "sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ==",
             "license": "ISC"
         },
         "node_modules/xtend": {
             "version": "4.0.2",
-            "resolved": "https://registry.npmjs.org/xtend/-/xtend-4.0.2.tgz",
-            "integrity": "sha512-LKYU1iAXJXUgAXn9URjiu+MWhyUXHsvfp7mcuYm9dSUKK0/CjtrUwFAxD82/mCWbtLsGjFIad0wIsod4zrTAEQ==",
             "license": "MIT",
             "engines": {
                 "node": ">=0.4"
@@ -15449,15 +13545,11 @@
         },
         "node_modules/yallist": {
             "version": "3.1.1",
-            "resolved": "https://registry.npmjs.org/yallist/-/yallist-3.1.1.tgz",
-            "integrity": "sha512-a4UGQaWPH59mOXUYnAG2ewncQS4i4F43Tv3JoAM+s2VDAmS9NsK8GpDMLrCHPksFT7h3K6TOoUNn2pb7RoXx4g==",
             "dev": true,
             "license": "ISC"
         },
         "node_modules/yaml": {
             "version": "1.10.2",
-            "resolved": "https://registry.npmjs.org/yaml/-/yaml-1.10.2.tgz",
-            "integrity": "sha512-r3vXyErRCYJ7wg28yvBY5VSoAF8ZvlcW9/BwUzEtUsjvX/DKs24dIkuwjtuprwJJHsbyUbLApepYTR1BN4uHrg==",
             "license": "ISC",
             "engines": {
                 "node": ">= 6"
@@ -15465,8 +13557,6 @@
         },
         "node_modules/yauzl": {
             "version": "2.10.0",
-            "resolved": "https://registry.npmjs.org/yauzl/-/yauzl-2.10.0.tgz",
-            "integrity": "sha512-p4a9I6X6nu6IhoGmBqAcbJy1mlC4j27vEPZX9F4L4/vZT3Lyq1VkFHw/V/PUcB9Buo+DG3iHkT0x3Qya58zc3g==",
             "dev": true,
             "license": "MIT",
             "dependencies": {
@@ -15476,8 +13566,6 @@
         },
         "node_modules/yjs": {
             "version": "13.6.27",
-            "resolved": "https://registry.npmjs.org/yjs/-/yjs-13.6.27.tgz",
-            "integrity": "sha512-OIDwaflOaq4wC6YlPBy2L6ceKeKuF7DeTxx+jPzv1FHn9tCZ0ZwSRnUBxD05E3yed46fv/FWJbvR+Ud7x0L7zw==",
             "license": "MIT",
             "peer": true,
             "dependencies": {
@@ -15494,8 +13582,6 @@
         },
         "node_modules/yocto-queue": {
             "version": "0.1.0",
-            "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz",
-            "integrity": "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==",
             "dev": true,
             "license": "MIT",
             "engines": {
@@ -15507,18 +13593,15 @@
         },
         "node_modules/zod": {
             "version": "4.1.13",
-            "resolved": "https://registry.npmjs.org/zod/-/zod-4.1.13.tgz",
-            "integrity": "sha512-AvvthqfqrAhNH9dnfmrfKzX5upOdjUVJYFqNSlkmGf64gRaTzlPwz99IHYnVs28qYAybvAlBV+H7pn0saFY4Ig==",
             "devOptional": true,
             "license": "MIT",
+            "peer": true,
             "funding": {
                 "url": "https://github.com/sponsors/colinhacks"
             }
         },
         "node_modules/zod-validation-error": {
             "version": "4.0.2",
-            "resolved": "https://registry.npmjs.org/zod-validation-error/-/zod-validation-error-4.0.2.tgz",
-            "integrity": "sha512-Q6/nZLe6jxuU80qb/4uJ4t5v2VEZ44lzQjPDhYJNztRQ4wyWc6VF3D3Kb/fAuPetZQnhS3hnajCf9CsWesghLQ==",
             "dev": true,
             "license": "MIT",
             "engines": {
@@ -15530,8 +13613,6 @@
         },
         "node_modules/zwitch": {
             "version": "2.0.4",
-            "resolved": "https://registry.npmjs.org/zwitch/-/zwitch-2.0.4.tgz",
-            "integrity": "sha512-bXE4cR/kVZhKZX/RjPEflHaKVhUVl85noU3v6b8apfQEc1x4A+zBxjZ4lN8LqGd6WZ3dl98pY4o717VFmoPp+A==",
             "license": "MIT",
             "funding": {
                 "type": "github",
diff --git a/ratings-ui-demo/package.json b/ratings-ui-demo/package.json
index 5e6484816066c8f46395e011b6bd0049e2d11d84..fc7ba144463d9fdedbfa68b34c8ed4ef81d2d987 100644
--- a/ratings-ui-demo/package.json
+++ b/ratings-ui-demo/package.json
@@ -29,7 +29,7 @@
         "@mui/x-date-pickers": "^8.21.0",
         "@mui/x-tree-view": "^8.21.0",
         "@next/mdx": "^16.0.7",
-        "@peerverity/ai-delegate": "^0.2.2",
+        "@peerverity/ai-delegate": "^0.3.0",
         "@uiw/react-codemirror": "^4.25.4",
         "axios": "^1.13.2",
         "cookies-next": "^6.1.1",
diff --git a/ratings-ui-demo/src/services/ai-service.ts b/ratings-ui-demo/src/services/ai-service.ts
index 6b319697289962f7cb2e31ba382b6088947d8163..021b71ddec050b8b03eadc89d2534fe9740bf04e 100644
--- a/ratings-ui-demo/src/services/ai-service.ts
+++ b/ratings-ui-demo/src/services/ai-service.ts
@@ -1,23 +1,46 @@
 /**
- * AI Service - Exact TypeScript port of Python ai_service.py
+ * AI Service - Ratings AI functionality using ai-delegate named-prompt framework
  *
- * This is a line-by-line port of ratings-api/services/ai_service.py
- * The ONLY difference is using AiDelegate.makeRequest() instead of direct Anthropic API calls
+ * This service uses the ai-delegate prompt management system for:
+ * - Named prompts with version control
+ * - User-customizable prompt templates
+ * - Structured response parsing
  */
 
-import AiDelegate from '@peerverity/ai-delegate';
+import AiDelegate, { Context } from '@peerverity/ai-delegate';
+import {
+  promptRegistrations,
+  getDatabaseSchemaContext,
+  getExtendedSchemaContext,
+  getPythonIntegrationContext
+} from './prompts';
 
 // Track initialization state
 let aiDelegateInitialized = false;
+let promptsRegistered = false;
 
 /**
- * Ensure AiDelegate is initialized before making requests
+ * Ensure AiDelegate is initialized and prompts are registered
  */
 async function ensureAiDelegateInitialized(): Promise<void> {
   if (!aiDelegateInitialized) {
     await AiDelegate.init({ endpoint: '/ai-delegate' });
     aiDelegateInitialized = true;
   }
+
+  // Register prompts if not already done
+  if (!promptsRegistered) {
+    const results = await AiDelegate.registerPrompts(promptRegistrations);
+
+    // Log any conflicts for debugging
+    for (const result of results) {
+      if (result.status === 'conflict') {
+        console.warn(`Prompt conflict for "${result.conflict.name}": local v${result.conflict.localVersion} vs upstream v${result.conflict.upstreamVersion}`);
+      }
+    }
+
+    promptsRegistered = true;
+  }
 }
 
 export type Weight = 'light' | 'moderate' | 'heavy';
@@ -109,8 +132,10 @@ export class AIService {
    * Generate SQL query for user selection from natural language.
    * The query must return a column aliased as 'user_id'.
    *
+   * Uses the 'user_selection_query' named prompt from the prompt registry.
+   *
    * @param naturalLanguage Natural language description of the desired user selection
-   * @param weight AI model weight
+   * @param weight AI model weight (note: prompt defines default weight, this can override)
    * @returns Dictionary with 'success', 'query', and optional 'error' fields
    */
   static async generateUserSelectionQuery(
@@ -118,55 +143,24 @@ export class AIService {
     weight: Weight = 'moderate'
   ): Promise<UserSelectionQueryResult> {
     try {
-      const schemaContext = AIService.getDatabaseSchemaContext();
-
-      const prompt = `You are a SQL query generator for selecting users from the PeerVerity database.
-Generate a SELECT query that returns user IDs based on the natural language description provided.
-
-${schemaContext}
-
-CRITICAL RULES FOR USER SELECTION QUERIES:
-1. The query MUST return exactly one column aliased as 'user_id'
-2. Use DISTINCT to avoid duplicate user IDs
-3. Only SELECT statements are allowed
-4. Use latest_valid_ratings view instead of ratings table for current ratings
-5. No semicolon at the end of the query
-
-EXAMPLES:
--- Users who rated a specific proposition above a threshold:
-SELECT DISTINCT lvr.creator_id AS user_id
-FROM latest_valid_ratings lvr
-WHERE lvr.proposition_id = 63 AND lvr.rating > 0.7
-
--- Users who delegated on a specific tag:
-SELECT DISTINCT d.creator_id AS user_id
-FROM delegations d
-JOIN tags t ON t.id = d.tag_id
-WHERE t.name = 'economics'
-
--- Human users only (excluding algorithms):
-SELECT id AS user_id
-FROM users
-WHERE kind = 'person'
-
--- Users who created propositions containing specific text:
-SELECT DISTINCT p.creator_id AS user_id
-FROM propositions p
-WHERE p.body ILIKE '%climate%'
-
-User request: ${naturalLanguage}
+      await ensureAiDelegateInitialized();
 
-Generate the SQL query (return ONLY the SQL, no explanations):`;
+      // Create context and append the named prompt with parameters
+      const ctx = AiDelegate.createContext();
+      await ctx.appendPrompt('user_selection_query', {
+        schemaContext: getDatabaseSchemaContext(),
+        naturalLanguage
+      });
 
-      await ensureAiDelegateInitialized();
-      const response = (await AiDelegate.makeRequest({
+      // Complete the context with the AI
+      await AiDelegate.completeContext(ctx, {
         weight,
         max_tokens: 1000,
-        temperature: 0,
-        messages: [{ role: 'user', content: prompt }],
-      })) as AiResponse;
+        temperature: 0
+      });
 
-      let generatedSql = response.content.trim();
+      // Get the response (last message content)
+      let generatedSql = (ctx.last as string).trim();
 
       // Strip Markdown code blocks if present
       const codeBlockMatch = generatedSql.match(/^```(?:sql)?\s*\n?([\s\S]*?)\n?```$/);
@@ -226,324 +220,39 @@ Generate the SQL query (return ONLY the SQL, no explanations):`;
     ];
   }
 
-  /**
-   * Get database schema context for SQL generation.
-   * Port of Python lines 14-146
-   */
-  private static getDatabaseSchemaContext(): string {
-    return `
-        PeerVerity Database Context:
-        PeerVerity is a system for rating the truth value of propositions (claims/statements) on a scale from 0 (false) to 1 (true).
-        Users can rate propositions, create arguments (inter-proposition links), and delegate voting power to experts.
-
-        CRITICAL DATABASE SCHEMA - PAY ATTENTION TO COLUMN NAMES:
-
-        Table: users
-        - id (integer, primary key): Unique user ID
-        - username (text): The user's username - THIS is the user identifier, NOT 'name'
-        - kind (text): Type of user ('person' for humans, 'algorithm' for system users, 'organization')
-        - email_alert_frequency (interval): Email alert frequency
-        - creation_time (timestamp): When the user was created
-        - creator_id (integer): ID of user who created this user (relevant for algorithm users)
-        - initweight (numeric): Initial weight for delegation algorithms (algorithm users only)
-        - hilimfac (numeric): High limit factor for delegation algorithms (algorithm users only)
-        - lolim (numeric): Low limit for delegation algorithms (algorithm users only)
-
-        Table: propositions
-        - id (integer, primary key): Unique proposition ID
-        - body (text): **THE ACTUAL PROPOSITION TEXT IS STORED HERE** - NOT in a column called 'proposition'!
-        - creation_time (timestamp): When the proposition was created
-        - creator_id (integer, foreign key to users.id): User who created the proposition
-
-        ⚠️ IMPORTANT: The proposition text is in the 'body' column, NOT 'proposition' column!
-
-        Table: ratings
-        - id (integer, primary key): Unique rating ID
-        - proposition_id (integer, foreign key to propositions.id): Proposition being rated
-        - rating (numeric): Rating value between 0 (false) and 1 (true)
-        - creation_time (timestamp): When the rating was created
-        - creator_id (integer, foreign key to users.id): User who created the rating
-
-        Table: latest_valid_ratings (view) - USE THIS INSTEAD OF ratings TABLE
-        - proposition_id (integer): Proposition ID
-        - rating (numeric): Rating value (0=false, 1=true)
-        - creator_id (integer): User ID who created the rating
-        - creation_time (timestamp): When the rating was created
-        Note: This view handles superseded ratings - users can update their ratings and this shows only the latest
-
-        Table: documents
-        - id (integer, primary key): Unique document ID
-        - title (text): Document title
-        - body (bytea): Document content (binary data)
-        - creation_time (timestamp): When the document was created
-        - creator_id (integer, foreign key to users.id): User who created the document
-
-        Table: tags
-        - id (integer, primary key): Unique tag ID
-        - name (text): The tag name - THIS is the tag text, NOT 'tag'
-        - useful_proposition_id (integer): Related proposition for tag usefulness
-        - creation_time (timestamp): When the tag was created
-        - creator_id (integer, foreign key to users.id): User who created the tag
-
-        Table: delegations
-        - id (integer, primary key): Unique delegation ID
-        - creator_id (integer, foreign key to users.id): User delegating their vote
-        - receiver_id (integer, foreign key to users.id): User receiving the delegation
-        - tag_id (integer, foreign key to tags.id): Tag for which delegation applies
-        - weight (numeric): Weight of the delegation
-        - creation_time (timestamp): When the delegation was created
-
-        Table: inter_proposition_links (arguments connecting propositions)
-        - id (integer, primary key): Unique link ID
-        - target_id (integer): Target proposition being supported/opposed
-        - origin_id (integer): Origin proposition providing the argument
-        - link_type (text): Type of link ('supports', 'opposes', 'rewording')
-        - impact_proposition_id (integer): Proposition rating the impact of this argument
-        - creator_id (integer): User who created the link
-        - creation_time (timestamp): When created
-
-        Table: document_proposition_links
-        - proposition_id (integer): Proposition being supported by document
-        - document_id (integer): Supporting document
-        - relevancy_proposition_id (integer): Proposition rating the relevancy
-        - creator_id (integer): User who created the link
-        - creation_time (timestamp): When created
-
-        Table: sources (original information sources)
-        - id (integer, primary key): Unique source ID
-        - name (text): Source name
-        - kind (text): Type ('person', 'ai', 'organization', 'bot', 'other')
-        - url (text): Source URL
-        - creator_id (integer): User who created this source
-        - creation_time (timestamp): When created
-
-        Table: cached_aggregate_ratings (pre-computed aggregate ratings per algorithm)
-        - id (integer, primary key): Unique record ID
-        - proposition_id (integer, foreign key to propositions.id): The proposition this rating is for
-        - creator_id (integer, foreign key to users.id): **The algorithm's user account** - links to rating_calculation_procedures
-        - aggregate_rating (numeric): The calculated rating value (0 = false, 1 = true)
-        - number_of_ratings (integer): Count of individual ratings used in calculation
-        - weight_of_ratings (numeric): Sum of weights used in calculation
-        - median_stat, mode_stat, stddev_stat, var_stat, min_stat, max_stat, range_stat, q1_stat, q3_stat, iqr_stat (numeric): Statistical values
-
-        ⚠️ IMPORTANT: cached_aggregate_ratings.creator_id is the ALGORITHM's user ID, NOT a human user!
-        To find ratings by algorithm name, join to rating_calculation_procedures on creator_id.
-
-        Table: rating_calculation_procedures (rating algorithms)
-        - id (integer, primary key): Algorithm ID
-        - name (text): Algorithm name displayed to users ('Mean', 'Median', 'Statistics', etc.)
-        - creator_id (integer, foreign key to users.id): **The algorithm's user account** - use this to join with cached_aggregate_ratings
-        - procedure_name (text): Internal procedure name
-        - is_enabled (boolean): Whether the algorithm is active
-        - description (text): Algorithm description
-
-        ⚠️ CRITICAL JOIN PATTERN for aggregate ratings by algorithm:
-        SELECT car.aggregate_rating
-        FROM cached_aggregate_ratings car
-        JOIN rating_calculation_procedures rcp ON car.creator_id = rcp.creator_id
-        WHERE rcp.name = 'Mean'  -- Use 'Mean', 'Median', 'Statistics', etc.
-
-        COMMON MISTAKES TO AVOID:
-        ❌ WRONG: WHERE p.proposition = 'some text'
-        ✅ RIGHT: WHERE p.body = 'some text'
-
-        ❌ WRONG: WHERE u.name = 'john'
-        ✅ RIGHT: WHERE u.username = 'john'
-
-        ❌ WRONG: WHERE t.tag = 'economics'
-        ✅ RIGHT: WHERE t.name = 'economics'
-
-        ❌ WRONG: JOIN calculated_ratings cr ON cr.proposition_id = p.id (table doesn't exist!)
-        ✅ RIGHT: JOIN cached_aggregate_ratings car ON car.proposition_id = p.id
-                  JOIN rating_calculation_procedures rcp ON car.creator_id = rcp.creator_id
-
-        ❌ WRONG: WHERE cr.algorithm = 'mean' (no 'algorithm' column exists)
-        ✅ RIGHT: WHERE rcp.name = 'Mean' (use rating_calculation_procedures.name)
-
-        CORRECT SQL EXAMPLES:
-        -- Finding users who rated a specific proposition by its text:
-        SELECT DISTINCT lvr.creator_id AS user_id
-        FROM latest_valid_ratings lvr
-        JOIN propositions p ON p.id = lvr.proposition_id
-        WHERE p.body = 'The earth is round';  -- Use 'body' column for proposition text!
-
-        -- Finding propositions containing specific words:
-        SELECT id AS proposition_id
-        FROM propositions
-        WHERE body ILIKE '%climate%';  -- Use 'body' for text search!
-
-        -- Finding human users only (not algorithms):
-        SELECT id AS user_id
-        FROM users
-        WHERE kind = 'person';  -- Use exact value 'person' not 'human'
-
-        -- Finding users who delegated on a specific tag:
-        SELECT DISTINCT d.creator_id AS user_id
-        FROM delegations d
-        JOIN tags t ON t.id = d.tag_id
-        WHERE t.name = 'economics';  -- Use 'name' column for tag text!
-
-        QUERY RULES:
-        1. ALWAYS return a column aliased as 'user_id' for user selection queries
-        2. Use latest_valid_ratings view, NOT the ratings table directly
-        3. Proposition text is in propositions.body column
-        4. Username is in users.username column
-        5. Tag text is in tags.name column
-        6. User kinds are: 'person' (humans), 'algorithm' (system users), 'organization'
-        7. Ratings are 0 (false) to 1 (true), where 0.5 is uncertain
-        `;
-  }
-
   /**
    * Generate SQL query for general data analysis from natural language.
-   * Exact port of Python lines 281-473
+   *
+   * Uses the 'analysis_query' named prompt from the prompt registry.
    *
    * @param naturalLanguage Natural language description of the desired analysis
-   * @param weight AI model weight (maps to Python's hardcoded model)
+   * @param weight AI model weight
    * @returns Dictionary with 'success', 'query', 'columns', and optional 'error' fields
    */
   static async generateAnalysisQuery(
     naturalLanguage: string,
-    weight: Weight = 'heavy'  // Default to 'heavy' to match Opus behavior
+    weight: Weight = 'heavy'
   ): Promise<AnalysisQueryResult> {
     try {
-      // Get database schema context (Python line 306)
-      const schemaContext = AIService.getDatabaseSchemaContext();
-
-      // Create prompt for analysis SQL generation (Python lines 309-418)
-      const prompt = `You are a SQL query generator for PeerVerity database analysis and reporting.
-            Generate a SELECT query based on the natural language description provided.
-
-            ${schemaContext}
-
-            ADDITIONAL TABLES AND VIEWS FOR ANALYSIS:
-
-            IMPORTANT: Override the default instruction about latest_valid_ratings:
-            - Use the 'ratings' table for historical analysis, time-series, and rating history
-            - Only use 'latest_valid_ratings' view when you specifically need just the most recent rating
-
-            Table: ratings (use this for historical analysis!)
-            - id (integer): Rating ID
-            - creator_id (integer): User who created the rating
-            - creation_time (timestamp): When the rating was created
-            - proposition_id (integer): The proposition being rated
-            - rating (numeric): Rating value (0 = false, 1 = true)
-
-            Table: rating_calculation_procedures (rating algorithms)
-            - id (integer): Algorithm ID
-            - name (text): Algorithm name displayed to users ('Mean', 'Median', 'Statistics', etc.)
-            - creator_id (integer): **The algorithm's user account** - use this to join with cached_aggregate_ratings
-            - procedure_name (text): Internal procedure name
-            - is_enabled (boolean): Whether the algorithm is active
-            - description (text): Algorithm description
-
-            Table: cached_aggregate_ratings (aggregated ratings per algorithm)
-            - proposition_id (integer): The proposition this rating is for
-            - creator_id (integer): **The algorithm's user ID** - join to rating_calculation_procedures.creator_id
-            - aggregate_rating (numeric): Calculated rating value (0 to 1)
-            - number_of_ratings (integer): Number of ratings used
-            - weight_of_ratings (numeric): Sum of weights used
-            - median_stat, mode_stat, stddev_stat, var_stat, min_stat, max_stat, range_stat, q1_stat, q3_stat, iqr_stat (numeric): Statistical values
-
-            ⚠️ CRITICAL JOIN PATTERN for aggregate ratings by algorithm:
-            SELECT car.aggregate_rating
-            FROM cached_aggregate_ratings car
-            JOIN rating_calculation_procedures rcp ON car.creator_id = rcp.creator_id
-            WHERE rcp.name = 'Mean'  -- Use 'Mean', 'Median', 'Statistics', etc.
-
-            Table: delegation_weights (calculated delegation weights)
-            - delegator_id (integer): User delegating
-            - receiver_id (integer): User receiving delegation
-            - tag_id (integer): Tag for delegation
-            - effective_weight (numeric): Calculated weight
-
-            CRITICAL INSTRUCTIONS:
-            1. Generate flexible SELECT queries that return data for analysis
-            2. Include meaningful column aliases for display
-            3. Use JOINs to include related data when helpful
-            4. Support time-based queries with date functions
-            5. Allow aggregations (COUNT, AVG, SUM, etc.)
-            6. Include ORDER BY for meaningful default sorting
-            7. Do not limit results unless specifically requested
-            8. Return columns with clear, descriptive aliases
-            9. Ensure queries are valid PostgreSQL syntax
-            10. No semicolon at the end
-            11. IMPORTANT: When aggregating from multiple one-to-many relationships (like summing incoming AND outgoing values), use subqueries to aggregate each relationship separately to avoid Cartesian products
-                Example: For delegations, instead of direct JOINs that cause duplication:
-                LEFT JOIN delegations d_out ON u.id = d_out.creator_id
-                LEFT JOIN delegations d_in ON u.id = d_in.receiver_id
-                Use subqueries:
-                LEFT JOIN (SELECT creator_id, SUM(weight) as total FROM delegations GROUP BY creator_id) d_out ON...
-                LEFT JOIN (SELECT receiver_id, SUM(weight) as total FROM delegations GROUP BY receiver_id) d_in ON...
-            12. CRITICAL - ALWAYS qualify ALL column names with table aliases to avoid ambiguity errors:
-                - In SELECT: Use r.creation_time, NOT just creation_time
-                - In WHERE: Use p.body, NOT just body
-                - In ORDER BY: Use r.creation_time, NOT just creation_time
-                - In aggregates: Use MIN(r2.creation_time), NOT MIN(creation_time)
-                - In subqueries: ALWAYS qualify - Use r2.creation_time for ratings alias r2
-                - NEVER write MIN(creation_time) - ALWAYS write MIN(r.creation_time) or MIN(r2.creation_time)
-                - This is MANDATORY for columns that exist in multiple tables:
-                  creation_time (exists in: users, propositions, ratings, documents, tags, delegations - ALWAYS QUALIFY!)
-                  creator_id (exists in most tables - ALWAYS QUALIFY!)
-                  id (exists in every table - ALWAYS QUALIFY!)
-                BAD: SELECT MIN(creation_time) FROM ratings r JOIN propositions p...
-                GOOD: SELECT MIN(r.creation_time) FROM ratings r JOIN propositions p...
-                Example: SELECT r.creation_time, r.rating FROM ratings r JOIN propositions p ON p.id = r.proposition_id ORDER BY r.creation_time
-            13. IMPORTANT: Return ONLY the SQL query, no explanatory text, no notes, just the pure SQL
-
-            EXAMPLES OF ANALYSIS QUERIES:
-
-            -- Ratings over time for a specific user and proposition
-            SELECT
-                DATE(r.creation_time) AS date,
-                r.rating AS rating_value,
-                p.body AS proposition_text
-            FROM latest_valid_ratings r
-            JOIN propositions p ON p.id = r.proposition_id
-            WHERE r.creator_id = 15 AND r.proposition_id = 42
-            ORDER BY r.creation_time
-
-            -- Top influential voters by tag
-            SELECT
-                u.username,
-                t.name AS tag_name,
-                SUM(dw.effective_weight) AS total_influence
-            FROM delegation_weights dw
-            JOIN users u ON u.id = dw.receiver_id
-            JOIN tags t ON t.id = dw.tag_id
-            GROUP BY u.username, t.name
-            ORDER BY total_influence DESC
-            LIMIT 10
-
-            -- Algorithm comparison for propositions
-            SELECT
-                p.id AS proposition_id,
-                p.body AS proposition,
-                MAX(CASE WHEN rcp.name = 'Mean' THEN car.aggregate_rating END) AS mean_rating,
-                MAX(CASE WHEN rcp.name = 'Median' THEN car.aggregate_rating END) AS median_rating,
-                MAX(CASE WHEN rcp.name = 'ComRating3' THEN car.aggregate_rating END) AS comrating3_rating
-            FROM propositions p
-            LEFT JOIN cached_aggregate_ratings car ON car.proposition_id = p.id
-            LEFT JOIN rating_calculation_procedures rcp ON car.creator_id = rcp.creator_id
-            GROUP BY p.id, p.body
-            ORDER BY p.id
-
-            User request: ${naturalLanguage}
-
-            Generate the SQL query:`;
-
-      // Call AI Delegate API (Python lines 420-428)
-      // Python uses: model="claude-opus-4-1-20250805", max_tokens=1500, temperature=0
       await ensureAiDelegateInitialized();
-      const response = (await AiDelegate.makeRequest({
-        weight,  // Map to weight instead of model
-        max_tokens: 1500,  // Exact match to Python
-        temperature: 0,    // Exact match to Python
-        messages: [{ role: 'user', content: prompt }],
-      })) as AiResponse;
 
-      // Extract the generated SQL (Python line 431)
-      let generatedSql = response.content.trim();
+      // Create context and append the named prompt with parameters
+      const ctx = AiDelegate.createContext();
+      await ctx.appendPrompt('analysis_query', {
+        schemaContext: getDatabaseSchemaContext(),
+        extendedSchemaContext: getExtendedSchemaContext(),
+        naturalLanguage
+      });
+
+      // Complete the context with the AI
+      await AiDelegate.completeContext(ctx, {
+        weight,
+        max_tokens: 1500,
+        temperature: 0
+      });
+
+      // Get the response
+      let generatedSql = (ctx.last as string).trim();
 
       // Strip Markdown code blocks if present
       const codeBlockMatch = generatedSql.match(/^```(?:sql)?\s*\n?([\s\S]*?)\n?```$/);
@@ -551,8 +260,6 @@ Generate the SQL query (return ONLY the SQL, no explanations):`;
         generatedSql = codeBlockMatch[1].trim();
       }
 
-
-
       // Basic validation - allow SELECT or WITH (CTEs)
       const upperSql = generatedSql.toUpperCase().trim();
       if (!upperSql.startsWith('SELECT') && !upperSql.startsWith('WITH')) {
@@ -564,11 +271,9 @@ Generate the SQL query (return ONLY the SQL, no explanations):`;
         };
       }
 
-      // Remove semicolon if present (Python line 443)
+      // Remove semicolon if present
       generatedSql = generatedSql.replace(/;+$/, '');
 
-      // Try to extract column information from the query (Python lines 445-451)
-      // Python returns empty array, but we can do better
       const columns: string[] = [];
 
       return {
@@ -578,7 +283,6 @@ Generate the SQL query (return ONLY the SQL, no explanations):`;
         error: undefined,
       };
     } catch (error) {
-      // Python lines 460-473
       return {
         success: false,
         error: `Unexpected error: ${error instanceof Error ? error.message : String(error)}`,
@@ -590,53 +294,50 @@ Generate the SQL query (return ONLY the SQL, no explanations):`;
 
   /**
    * Analyze data with conversation history for multi-turn interactions.
-   * Exact port of Python lines 839-1297
+   *
+   * Uses named prompts: 'analyze_data_with_results', 'analyze_data_initial', 'analyze_context_followup'
    *
    * @param question Current natural language question about the data
    * @param conversationHistory List of previous exchanges
    * @param tableData Optional current table data with columns and rows
-   * @param weight AI model weight (maps to Python's model parameter)
+   * @param weight AI model weight
    * @returns Dictionary with 'success', 'answer', 'sql_query', 'needs_execution', and optional 'error' fields
    */
   static async analyzeDataConversation(
     question: string,
     conversationHistory?: ConversationMessage[],
     tableData?: TableData,
-    weight: Weight = 'heavy'  // Default to 'heavy' to match Opus
+    weight: Weight = 'heavy'
   ): Promise<ConversationAnalysisResult> {
     try {
-      // Get database schema context (Python line 873)
-      const schemaContext = AIService.getDatabaseSchemaContext();
+      await ensureAiDelegateInitialized();
 
-      // Prepare table data context if provided (Python lines 876-897)
+      // Prepare table data context if provided
       let tableContext = '';
       if (tableData && tableData.columns && tableData.rows) {
         const columns = tableData.columns;
         const allRows = tableData.rows;
-
-        // For analysis, include ALL rows if reasonable, otherwise limit (Python line 882)
         const rowsToShow = allRows.length <= 50 ? allRows : allRows.slice(0, 50);
 
         tableContext = `
 
-                QUERY RESULTS - ACTUAL DATA FROM PREVIOUS QUERY:
-                Total rows returned: ${allRows.length}
-                Columns: ${columns.map(col => `${col.name} (${col.type})`).join(', ')}
+QUERY RESULTS - ACTUAL DATA FROM PREVIOUS QUERY:
+Total rows returned: ${allRows.length}
+Columns: ${columns.map(col => `${col.name} (${col.type})`).join(', ')}
 
-                ${rowsToShow.length === allRows.length ? 'ALL' : `First ${rowsToShow.length} of ${allRows.length}`} rows of actual data:
-                ${JSON.stringify(rowsToShow, null, 2)}
+${rowsToShow.length === allRows.length ? 'ALL' : `First ${rowsToShow.length} of ${allRows.length}`} rows of actual data:
+${JSON.stringify(rowsToShow, null, 2)}
 
-                IMPORTANT: This is the ACTUAL data from a previous query. When referencing it:
-                - ONLY reference items that appear in this data
-                - Quote exact text/values from these results
-                - If you need to mention specific examples, they MUST come from this data
-                `;
+IMPORTANT: This is the ACTUAL data from a previous query. When referencing it:
+- ONLY reference items that appear in this data
+- Quote exact text/values from these results
+- If you need to mention specific examples, they MUST come from this data
+`;
       }
 
-      // Build conversation context (Python lines 899-917)
+      // Build conversation context
       let convContext = '';
       if (conversationHistory) {
-        // Take last 10 exchanges (20 messages total) (Python line 903)
         const recentHistory =
           conversationHistory.length > 20
             ? conversationHistory.slice(-20)
@@ -649,133 +350,40 @@ Generate the SQL query (return ONLY the SQL, no explanations):`;
         }
 
         convContext += `
-                CONVERSATION CONTEXT NOTES:
-                - This is a continuation of the above conversation
-                - Reference previous questions and answers when relevant
-                - Build upon insights already discovered
-                - You can refer to "as we discussed" or "as shown earlier" when appropriate
-                - If the user asks for clarification or follow-up, reference the specific previous response
-                `;
+CONVERSATION CONTEXT NOTES:
+- This is a continuation of the above conversation
+- Reference previous questions and answers when relevant
+- Build upon insights already discovered
+- You can refer to "as we discussed" or "as shown earlier" when appropriate
+- If the user asks for clarification or follow-up, reference the specific previous response
+`;
       }
 
-      // Check if we have actual query results (Python line 920)
+      // Check if we have actual query results
       const hasQueryResults =
         tableData &&
         tableData.rows &&
         tableData.rows.length > 0;
 
-      // Create the prompt based on whether we need to generate SQL or analyze existing data
-      // (Python line 922)
       if (hasQueryResults) {
-        // We have data from a previous query - analyze it in conversation context
-        // Python lines 925-1037 (PHASE 2)
-        const prompt = `You are a conversational data analyst with access to the PeerVerity database.
-
-                DATABASE SCHEMA:
-                ${schemaContext}
-
-                ${convContext}
-
-                ${tableContext}
-
-                CURRENT USER QUESTION: ${question}
-
-                PYTHON INTEGRATION (plpython3u):
-                PostgreSQL has plpython3u enabled, allowing Python functions for complex calculations.
-
-                IMPORTANT: If the user explicitly mentions "Python", "in Python", "using Python",
-                "with Python", "plpython3u", etc., you MUST use Python integration even if SQL alone could work.
-                User preference for implementation method always takes priority.
-
-                When to use Python:
-                1. User explicitly requests it (ALWAYS honor this - highest priority)
-                2. Complex mathematical operations (statistics, custom algorithms, advanced rounding)
-                3. Data transformations that are clearer in Python than SQL
-                4. Multi-step calculations easier to express in procedural code
-
-                Pattern for Python functions:
-                CREATE OR REPLACE FUNCTION function_name(param1 type1, param2 type2)
-                RETURNS return_type AS $$
-                    # Section 1: Extract data from database (if needed)
-                    query = f"SELECT column FROM table WHERE id = {{param1}}"
-                    result = plpy.execute(query)
-                    data = [row['column'] for row in result]
-
-                    # Section 2: Python calculations
-                    calculated_value = your_python_logic(data)
-
-                    # Section 3: Return result
-                    return calculated_value
-$$ LANGUAGE plpython3u;
-
-                Simple example - Rounding to tenth:
-                CREATE OR REPLACE FUNCTION round_to_tenth(value numeric)
-                RETURNS numeric AS $$
-                    import decimal
-                    val = float(value)
-                    rounded = round(val, 1)  # Round to 1 decimal place
-                    return decimal.Decimal(str(rounded))
-$$ LANGUAGE plpython3u;
-
-                Usage in query:
-                SELECT proposition_id, round_to_tenth(aggregate_rating) AS rounded_rating
-                FROM cached_aggregate_ratings;
-
-                For more complex examples, see the database functions:
-                - python_rating_algorithm(creator_id, proposition_id) - median calculation
-                - python_statistics_algorithm(creator_id, proposition_id) - comprehensive statistics
-
-                INSTRUCTIONS FOR CONVERSATIONAL ANALYSIS:
-                1. This is part of an ongoing conversation - reference previous context when relevant
-                2. The data shown above is from a previous SQL query in this conversation
-                3. ONLY describe what you see in the actual data provided
-                4. When users ask for "all", "every", "list each" - provide COMPLETE lists, don't summarize
-                5. Only summarize if listing more than 50 items (and clearly state this)
-                6. DO NOT make up or imagine examples that aren't in the data
-                7. DO NOT hallucinate counter-arguments or content not present in the results
-                8. When mentioning specific items (like counter-arguments, propositions, etc.), QUOTE EXACTLY from the data
-                9. If the data shows counter-arguments, list ONLY the ones actually present in the results
-                10. Use phrases like "The data shows..." or "According to the results..." to ground your response
-                11. If something isn't in the provided data, say "The data doesn't show..." or "Not found in these results"
-                12. Build upon previous insights if this is a follow-up question
-                13. If the user asks to clarify or expand on something, reference your previous response
-                14. If asked about something not in the current data, you MUST generate a new SQL query
-                15. Be conversational but precise - quote exact values from the data
-                16. CRITICAL: All SQL queries are executed AUTOMATICALLY - never ask for permission
-                17. NEVER ask "Would you like me to execute?" or similar questions
-                18. If you need more data, just say you're retrieving it and provide the SQL
-                19. When you generate a new SQL query, include it in your response like this:
-                    "I'll retrieve that information for you using the following SQL query:
-                    \`\`\`sql
-                    [YOUR SQL HERE]
-                    \`\`\`"
-                20. For rating-related queries:
-                    - Use 'ratings' table for: history, time-series, changes over time, tracking rating evolution
-                    - Use 'latest_valid_ratings' view ONLY for: current state, most recent rating per user
-                    - When users mention "history", "over time", "changes", "evolution" → use 'ratings' table
-                    - Include creation_time to track when ratings occurred
-                21. When user requests Python calculations in new queries:
-                    - First CREATE the Python function with plpython3u
-                    - Then use that function in your SELECT query
-                    - Both the CREATE and SELECT can be included in the query (separated by semicolon)
-
-                Respond naturally as part of the conversation. If you need to run a new query to answer the question,
-                just state that you're executing it - DO NOT ask for permission. The system will handle execution automatically.
-                `;
-
-        // Python lines 1019-1026
-        await ensureAiDelegateInitialized();
-        const response = (await AiDelegate.makeRequest({
-          weight: 'heavy',  // Python hardcodes to opus for Phase 2
-          max_tokens: 2000,  // Exact match to Python
-          temperature: 0,    // Exact match to Python
-          messages: [{ role: 'user', content: prompt }],
-        })) as AiResponse;
-
-        const answerText = response.content.trim();
-
-        // For conversational mode with existing data, usually no new SQL needed
-        // Python lines 1031-1037
+        // Phase 2: Analyze existing data using named prompt
+        const ctx = AiDelegate.createContext();
+        await ctx.appendPrompt('analyze_data_with_results', {
+          schemaContext: getDatabaseSchemaContext(),
+          conversationContext: convContext,
+          tableContext,
+          naturalLanguage: question,
+          pythonIntegrationContext: getPythonIntegrationContext()
+        });
+
+        await AiDelegate.completeContext(ctx, {
+          weight: 'heavy',
+          max_tokens: 2000,
+          temperature: 0
+        });
+
+        const answerText = (ctx.last as string).trim();
+
         return {
           success: true,
           answer: answerText,
@@ -784,172 +392,25 @@ $$ LANGUAGE plpython3u;
           error: undefined,
         };
       } else {
-        // No data yet - need to determine if SQL is needed
-        // Python lines 1040-1280 (PHASE 1)
-        const prompt = `You are a conversational data analyst with FULL ACCESS to the PeerVerity database.
-
-                DATABASE SCHEMA:
-                ${schemaContext}
-
-                ${convContext}
-
-                CURRENT USER QUESTION: ${question}
-
-                PYTHON INTEGRATION (plpython3u):
-                PostgreSQL has plpython3u enabled, allowing Python functions for complex calculations.
-
-                IMPORTANT: If the user explicitly mentions "Python", "in Python", "using Python",
-                "with Python", "plpython3u", etc., you MUST use Python integration even if SQL alone could work.
-                User preference for implementation method always takes priority.
-
-                When to use Python:
-                1. User explicitly requests it (ALWAYS honor this - highest priority)
-                2. Complex mathematical operations (statistics, custom algorithms, advanced rounding)
-                3. Data transformations that are clearer in Python than SQL
-                4. Multi-step calculations easier to express in procedural code
-
-                Pattern for Python functions:
-                CREATE OR REPLACE FUNCTION function_name(param1 type1, param2 type2)
-                RETURNS return_type AS $$
-                    # Section 1: Extract data from database (if needed)
-                    query = f"SELECT column FROM table WHERE id = {{param1}}"
-                    result = plpy.execute(query)
-                    data = [row['column'] for row in result]
-
-                    # Section 2: Python calculations
-                    calculated_value = your_python_logic(data)
-
-                    # Section 3: Return result
-                    return calculated_value
-$$ LANGUAGE plpython3u;
-
-                Simple example - Rounding to tenth:
-                CREATE OR REPLACE FUNCTION round_to_tenth(value numeric)
-                RETURNS numeric AS $$
-                    import decimal
-                    val = float(value)
-                    rounded = round(val, 1)  # Round to 1 decimal place
-                    return decimal.Decimal(str(rounded))
-$$ LANGUAGE plpython3u;
-
-                Usage in query:
-                SELECT proposition_id, round_to_tenth(aggregate_rating) AS rounded_rating
-                FROM cached_aggregate_ratings;
-
-                For more complex examples, see the database functions:
-                - python_rating_algorithm(creator_id, proposition_id) - median calculation
-                - python_statistics_algorithm(creator_id, proposition_id) - comprehensive statistics
-
-                INSTRUCTIONS:
-                1. This is ${conversationHistory ? 'a continuation of' : 'the start of'} a conversation
-                2. IMPORTANT: Check if you can answer from the conversation history BEFORE generating SQL
-                3. If the user references "these", "those", "the above", "of the X items" - they're referring to previous results
-                4. Only generate SQL if you need NEW data not already in the conversation
-                5. If the user asks to filter, categorize, or analyze data already shown, answer directly without SQL
-                6. You have complete access to the PeerVerity database when new data is needed
-                7. Generate SQL when asked about NEW specific content not already discussed
-                8. Exploratory questions about NEW topics need SQL to provide real answers
-                9. Reference previous conversation context when relevant
-                10. Build upon insights from earlier in the conversation
-                11. CRITICAL: All SQL queries are executed AUTOMATICALLY - never ask for permission
-                12. NEVER ask "Would you like me to execute?" or "Shall I run this query?"
-                13. Your response assumes the query is already being executed IF SQL is needed
-                14. For questions about counter-arguments or sub-arguments:
-                    - Use inter_proposition_links table with link_type = 'opposes' for counter-arguments
-                    - Join with propositions to get the actual argument text from the body column
-                    - Include creation_time to understand timing of arguments
-                15. IMPORTANT for rating queries - Override the default about latest_valid_ratings:
-                    - Use 'ratings' table for: history, time-series, changes over time, tracking evolution
-                    - Use 'latest_valid_ratings' view ONLY for: current state, most recent rating per user
-                    - Keywords like "history", "over time", "changes", "track" → use 'ratings' table
-                    - Always include creation_time when using 'ratings' table
-                16. When user requests Python calculations:
-                    - First CREATE the Python function with plpython3u
-                    - Then use that function in your SELECT query
-                    - Both the CREATE and SELECT can be included in sql_query (separated by semicolon)
-                    - Or return just the CREATE if only defining the function
-                17. IMPORTANT for UNION queries:
-                    - All columns in UNION must have matching types
-                    - Use explicit casts when combining json/jsonb: column::json or column::jsonb
-                    - When using json_build_object() with UNION, ensure all branches return the same type
-                    - Prefer simple column-based queries over complex UNIONs when possible
-
-                Please respond in JSON format:
-                {
-                    "needs_sql": true/false,
-                    "sql_query": "SELECT ... (REQUIRED for any question about specific data/content/users)",
-                    "answer_preview": "When SQL is needed, include it in your response like: 'I'll retrieve that information using this SQL query:\\n\`\`\`sql\\n[SQL HERE]\\n\`\`\`\\n\\nExecuting the query now...' Otherwise just provide the answer.",
-                    "references_previous": true/false (does this build on previous conversation?)
-                }
-
-                Examples of questions that MUST have needs_sql=true:
-                - "Tell me about the socialism discussion" (new topic not discussed)
-                - "What has alice rated?" (new query about user)
-                - "Summarize the database content" (exploratory new query)
-                - "Who are the participants in any discussion?" (new data needed)
-                - "What propositions exist about climate?" (new topic query)
-
-                Examples of questions that should have needs_sql=false (answer from context):
-                - "Of these 34 propositions, which are about animals?" (filtering previous results)
-                - "What was the highest rating you mentioned?" (referencing previous answer)
-                - "Can you categorize those users by type?" (analyzing shown data)
-                - "Explain more about the third item" (elaborating on previous results)
-
-                IMPORTANT: When generating SQL, include it in answer_preview. Examples:
-                - GOOD with SQL: "I'll retrieve the rating history using this query:\n\`\`\`sql\nSELECT ...\n\`\`\`\n\nExecuting now..."
-                - GOOD without SQL: "Based on the previous results, there are 5 users of type 'person'..."
-                - BAD: "Would you like me to retrieve the data?"
-                - BAD: "Should I execute this query?"
-
-                Example SQL for finding counter-arguments:
-                SELECT
-                    p_target.body AS target_proposition,
-                    p_origin.body AS counter_argument,
-                    ipl.creation_time,
-                    u.username AS argument_creator
-                FROM propositions p_target
-                JOIN inter_proposition_links ipl ON p_target.id = ipl.target_id
-                JOIN propositions p_origin ON ipl.origin_id = p_origin.id
-                LEFT JOIN users u ON ipl.creator_id = u.id
-                WHERE p_target.body ILIKE '%specific text%'
-                    AND ipl.link_type = 'opposes'
-                ORDER BY ipl.creation_time;
-
-                Example SQL for rating history (use 'ratings' table):
-                SELECT r.creation_time, r.rating, u.username
-                FROM ratings r
-                JOIN propositions p ON r.proposition_id = p.id
-                JOIN users u ON r.creator_id = u.id
-                WHERE p.body = 'Global warming is bad'
-                ORDER BY r.creation_time;
-
-                Example SQL for database summary (use simple counts, avoid complex UNIONs):
-                SELECT
-                    (SELECT COUNT(*) FROM users) as user_count,
-                    (SELECT COUNT(*) FROM users WHERE kind = 'person') as human_users,
-                    (SELECT COUNT(*) FROM users WHERE kind = 'algorithm') as algorithm_users,
-                    (SELECT COUNT(*) FROM propositions) as proposition_count,
-                    (SELECT COUNT(*) FROM ratings) as rating_count,
-                    (SELECT COUNT(*) FROM tags) as tag_count,
-                    (SELECT COUNT(*) FROM documents) as document_count,
-                    (SELECT COUNT(*) FROM delegations) as delegation_count,
-                    (SELECT COUNT(*) FROM sources) as source_count;
-
-                Remember: SQL execution is automatic. Never ask for permission.
-                `;
-
-        // Python lines 1176-1183
-        await ensureAiDelegateInitialized();
-        const response = (await AiDelegate.makeRequest({
-          weight,  // Use provided weight (maps to Python's model parameter)
-          max_tokens: 1500,  // Exact match to Python
-          temperature: 0,    // Exact match to Python
-          messages: [{ role: 'user', content: prompt }],
-        })) as AiResponse;
-
-        const responseText = response.content.trim();
-
-        // Parse JSON response (Python lines 1188-1227)
+        // Phase 1: Determine if SQL is needed using named prompt
+        const ctx = AiDelegate.createContext();
+        await ctx.appendPrompt('analyze_data_initial', {
+          schemaContext: getDatabaseSchemaContext(),
+          conversationContext: convContext,
+          naturalLanguage: question,
+          pythonIntegrationContext: getPythonIntegrationContext(),
+          conversationStatus: conversationHistory ? 'a continuation of' : 'the start of'
+        });
+
+        await AiDelegate.completeContext(ctx, {
+          weight,
+          max_tokens: 1500,
+          temperature: 0
+        });
+
+        const responseText = (ctx.last as string).trim();
+
+        // Parse JSON response
         let analysisPlan: {
           needs_sql?: boolean;
           sql_query?: string;
@@ -957,13 +418,12 @@ $$ LANGUAGE plpython3u;
           references_previous?: boolean;
         } = {};
 
-        // Strategy 1: Extract JSON block (Python line 1191)
         const jsonMatch = responseText.match(/\{[\s\S]*\}/);
         if (jsonMatch) {
           try {
             let jsonStr = jsonMatch[0];
 
-            // Strategy 2: Clean up SQL in JSON (Python lines 1196-1207)
+            // Clean up SQL in JSON
             jsonStr = jsonStr.replace(
               /"sql_query"\s*:\s*"((?:[^"\\]|\\.)*)"/g,
               (match, sqlContent) => {
@@ -977,13 +437,11 @@ $$ LANGUAGE plpython3u;
 
             analysisPlan = JSON.parse(jsonStr);
 
-            // Strategy 3: CRITICAL: Always execute SQL if one was generated (Python line 680)
             if (analysisPlan.sql_query) {
               analysisPlan.needs_sql = true;
             }
           } catch {
-            // Strategy 4: Fallback - lenient parsing (Python lines 1211-1219)
-            // Try to extract answer_preview even when JSON parsing fails
+            // Fallback - lenient parsing
             const previewMatch = responseText.match(/"answer_preview"\s*:\s*"([^"]+)"/);
             analysisPlan = {
               needs_sql: responseText.toUpperCase().includes('SELECT'),
@@ -994,10 +452,10 @@ $$ LANGUAGE plpython3u;
               references_previous: !!conversationHistory,
             };
 
-            // Strategy 5: Try to extract SQL query if present (Python lines 1692-1708)
+            // Try to extract SQL query if present
             const sqlPatterns = [
-              /"sql_query"\s*:\s*"([^"]+(?:\\"[^"]+)*)"/,  // JSON-style
-              /SELECT\s+[\s\S]*?(?:;|\n\n|$)/i,            // Raw SQL
+              /"sql_query"\s*:\s*"([^"]+(?:\\"[^"]+)*)"/,
+              /SELECT\s+[\s\S]*?(?:;|\n\n|$)/i,
             ];
 
             for (const pattern of sqlPatterns) {
@@ -1008,7 +466,6 @@ $$ LANGUAGE plpython3u;
                     ? sqlMatch[1]
                     : sqlMatch[0];
 
-                // Clean up
                 sqlQuery = sqlQuery
                   .trim()
                   .replace(/;+$/, '')
@@ -1023,7 +480,6 @@ $$ LANGUAGE plpython3u;
             }
           }
         } else {
-          // Strategy 6: No JSON found (Python lines 1220-1227)
           analysisPlan = {
             needs_sql: false,
             sql_query: undefined,
@@ -1032,55 +488,36 @@ $$ LANGUAGE plpython3u;
           };
         }
 
-        // Final enforcement: Force needs_sql if query exists (Python line 711-714)
         if (analysisPlan.sql_query) {
           console.log('SQL query found, forcing needs_sql=True');
           analysisPlan.needs_sql = true;
         }
 
-        // Generate final answer (Python lines 1230-1271)
+        // Generate final answer
         let finalAnswer: string;
         let needsExecution: boolean;
 
         if (analysisPlan.needs_sql && analysisPlan.sql_query) {
-          // SQL needs to be executed (Python lines 1231-1233)
           finalAnswer =
             analysisPlan.answer_preview || '🔄 Executing query to retrieve the requested data...';
           needsExecution = true;
         } else {
-          // Need to generate full answer from context (not just preview)
-          // Python lines 1236-1270
           if (conversationHistory && conversationHistory.length > 0) {
-            // Generate the full answer from conversation context (Python lines 1238-1266)
-            const contextPrompt = `You are analyzing a conversation and need to answer based on previous context.
-
-                        PREVIOUS CONVERSATION:
-                        ${convContext}
-
-                        CURRENT QUESTION: ${question}
-
-                        INSTRUCTIONS:
-                        1. Answer based ONLY on information from the previous conversation
-                        2. When asked to list items, provide the COMPLETE list - don't summarize
-                        3. If asked for "every" or "all" items, list them explicitly
-                        4. Only summarize if there are more than 50 items (and state this)
-                        5. Be specific and comprehensive in your response
-                        6. If filtering or categorizing, show the exact items that match
-
-                        Provide the complete answer based on the conversation history.
-                        `;
-
-            await ensureAiDelegateInitialized();
-            const contextResponse = (await AiDelegate.makeRequest({
-              weight: 'heavy',  // Python hardcodes to opus
-              max_tokens: 2000,  // Exact match to Python
-              temperature: 0,    // Exact match to Python
-              messages: [{ role: 'user', content: contextPrompt }],
-            })) as AiResponse;
-
-            finalAnswer = contextResponse.content.trim();
+            // Generate full answer from conversation context using named prompt
+            const followupCtx = AiDelegate.createContext();
+            await followupCtx.appendPrompt('analyze_context_followup', {
+              conversationContext: convContext,
+              naturalLanguage: question
+            });
+
+            await AiDelegate.completeContext(followupCtx, {
+              weight: 'heavy',
+              max_tokens: 2000,
+              temperature: 0
+            });
+
+            finalAnswer = (followupCtx.last as string).trim();
           } else {
-            // No conversation history, use the preview or response text (Python lines 1268-1269)
             finalAnswer = analysisPlan.answer_preview || responseText;
           }
 
@@ -1089,9 +526,9 @@ $$ LANGUAGE plpython3u;
 
         // Safeguard: Ensure we never return raw JSON as the answer
         if (finalAnswer.trim().startsWith('{') && finalAnswer.includes('"needs_sql"')) {
-          const jsonMatch = finalAnswer.match(/"answer_preview"\s*:\s*"((?:[^"\\]|\\.)*)"/);
-          if (jsonMatch) {
-            finalAnswer = jsonMatch[1].replace(/\\n/g, '\n').replace(/\\"/g, '"');
+          const extractedMatch = finalAnswer.match(/"answer_preview"\s*:\s*"((?:[^"\\]|\\.)*)"/);
+          if (extractedMatch) {
+            finalAnswer = extractedMatch[1].replace(/\\n/g, '\n').replace(/\\"/g, '"');
           } else {
             finalAnswer = needsExecution
               ? 'Retrieving the requested information...'
@@ -1099,7 +536,6 @@ $$ LANGUAGE plpython3u;
           }
         }
 
-        // Python lines 1273-1280
         return {
           success: true,
           answer: finalAnswer,
@@ -1110,7 +546,6 @@ $$ LANGUAGE plpython3u;
         };
       }
     } catch (error) {
-      // Python lines 1282-1297
       return {
         success: false,
         error: `Unexpected error: ${error instanceof Error ? error.message : String(error)}`,
@@ -1125,6 +560,8 @@ $$ LANGUAGE plpython3u;
    * Generate SQL query for event detection from natural language.
    * This is used by the AI Event Builder to create custom event definitions.
    *
+   * Uses the 'event_query' named prompt from the prompt registry.
+   *
    * @param naturalLanguage Natural language description of the event to detect
    * @param conversationHistory Previous conversation messages for context
    * @param weight AI model weight
@@ -1136,7 +573,7 @@ $$ LANGUAGE plpython3u;
     weight: Weight = 'heavy'
   ): Promise<EventQueryResult> {
     try {
-      const schemaContext = AIService.getDatabaseSchemaContext();
+      await ensureAiDelegateInitialized();
 
       // Build conversation context if there's history
       let conversationContext = '';
@@ -1149,115 +586,22 @@ Continue the conversation and generate the event query based on this context.
 `;
       }
 
-      const prompt = `You are an AI assistant that helps users create custom event detection queries for the PeerVerity system.
-Users want to receive notifications when certain conditions are met in the database.
-
-${schemaContext}
+      // Create context and append the named prompt
+      const ctx = AiDelegate.createContext();
+      await ctx.appendPrompt('event_query', {
+        schemaContext: getDatabaseSchemaContext(),
+        conversationContext,
+        naturalLanguage
+      });
 
-ADDITIONAL CONTEXT FOR EVENT QUERIES:
-
-Table: followed_propositions
-- id (integer): Follow record ID
-- creator_id (integer): User who is following
-- proposition_id (integer): Proposition being followed
-- event_types (text[]): Types of events to track
-- creation_time (timestamp): When follow was created
-
-Table: followed_tags
-- id (integer): Follow record ID
-- creator_id (integer): User who is following
-- tag_id (integer): Tag being followed
-- event_types (text[]): Types of events to track
-- creation_time (timestamp): When follow was created
-
-Table: followed_documents
-- id (integer): Follow record ID
-- creator_id (integer): User who is following
-- document_id (integer): Document being followed
-- event_types (text[]): Types of events to track
-- creation_time (timestamp): When follow was created
-
-EVENT QUERY CONTRACT:
-The SQL query you generate should return rows that represent entities currently meeting the event condition.
-Each row returned means "this entity currently triggers the event."
-
-Required output columns:
-- entity_type (text): The type of entity (e.g., 'proposition', 'tag', 'user', 'document')
-- entity_id (integer): The ID of the entity
-- display_text (text): A human-readable description for the notification feed
-
-The system will:
-1. Run this query periodically (e.g., every 10 seconds)
-2. Compare results to previous run
-3. Create event notifications for NEW rows (entities that newly meet the condition)
-4. Optionally create notifications for rows that disappeared (entities that no longer meet the condition)
-
-EXAMPLES:
-
--- Event: Proposition receives more than 10 ratings in the last hour
-SELECT
-    'proposition'::text AS entity_type,
-    p.id AS entity_id,
-    'Proposition "' || LEFT(p.body, 50) || '..." received more than 10 ratings in the last hour' AS display_text
-FROM propositions p
-WHERE (
-    SELECT COUNT(*)
-    FROM ratings r
-    WHERE r.proposition_id = p.id
-    AND r.creation_time > NOW() - INTERVAL '1 hour'
-) > 10;
-
--- Event: User creates a new proposition
-SELECT
-    'proposition'::text AS entity_type,
-    p.id AS entity_id,
-    'User "' || u.username || '" created proposition: "' || LEFT(p.body, 50) || '..."' AS display_text
-FROM propositions p
-JOIN users u ON u.id = p.creator_id
-WHERE p.creation_time > NOW() - INTERVAL '1 minute';
-
--- Event: Proposition aggregate rating (using Mean algorithm) exceeds threshold
-SELECT
-    'proposition'::text AS entity_type,
-    p.id AS entity_id,
-    'Proposition "' || LEFT(p.body, 50) || '..." reached ' || ROUND(car.aggregate_rating * 100) || '% rating' AS display_text
-FROM propositions p
-JOIN cached_aggregate_ratings car ON car.proposition_id = p.id
-JOIN rating_calculation_procedures rcp ON car.creator_id = rcp.creator_id
-WHERE rcp.name = 'Mean' AND car.aggregate_rating > 0.8;
-
--- Event: Tag is used more than 5 times in a day
-SELECT
-    'tag'::text AS entity_type,
-    t.id AS entity_id,
-    'Tag "' || t.name || '" was used ' || COUNT(*) || ' times today' AS display_text
-FROM tags t
-JOIN proposition_tag_links ptl ON ptl.tag_id = t.id
-WHERE ptl.creation_time > NOW() - INTERVAL '1 day'
-GROUP BY t.id, t.name
-HAVING COUNT(*) > 5;
-
-${conversationContext}
-
-User request: ${naturalLanguage}
-
-Generate a SQL query that detects this event condition. Return ONLY the SQL query, followed by a brief explanation of what it does.
-Format your response as:
-\`\`\`sql
-<your SQL query here>
-\`\`\`
-
-Explanation: <brief explanation of what the query detects>`;
-
-      await ensureAiDelegateInitialized();
-      const response = (await AiDelegate.makeRequest({
+      // Complete the context with the AI
+      await AiDelegate.completeContext(ctx, {
         weight,
         max_tokens: 2000,
-        temperature: 0,
-        messages: [{ role: 'user', content: prompt }],
-      })) as AiResponse;
+        temperature: 0
+      });
 
-      const responseContent = response.content.trim();
+      const responseContent = (ctx.last as string).trim();
 
       // Extract SQL from markdown code block
       const codeBlockMatch = responseContent.match(/```(?:sql)?\s*\n?([\s\S]*?)\n?```/);
@@ -1329,7 +673,7 @@ Explanation: <brief explanation of what the query detects>`;
     conversationHistory: Array<{ role: string; content: string }> = [],
     customBasePrompt?: string
   ): string {
-    const schemaContext = AIService.getDatabaseSchemaContext();
+    const schemaContext = getDatabaseSchemaContext();
 
     // Build conversation context if there's history
     let conversationContext = '';
@@ -1731,6 +1075,8 @@ Explanation: <brief description of what this generator produces>`;
    * Generate SQL query and MDX template for a feed generator from natural language.
    * This is used by the AI Feed Generator Builder to create custom feed generators.
    *
+   * Uses Context API with either custom prompt or the built-in feed generator prompt.
+   *
    * @param naturalLanguage Natural language description of the feed generator to create
    * @param conversationHistory Previous conversation messages for context
    * @param weight AI model weight
@@ -1746,17 +1092,24 @@ Explanation: <brief description of what this generator produces>`;
     customBasePrompt?: string
   ): Promise<FeedGeneratorQueryResult> {
     try {
-      const prompt = customPrompt || AIService.buildFeedGeneratorPrompt(naturalLanguage, conversationHistory, customBasePrompt);
-
       await ensureAiDelegateInitialized();
-      const response = (await AiDelegate.makeRequest({
+
+      // Build the prompt content
+      const promptContent = customPrompt || AIService.buildFeedGeneratorPrompt(naturalLanguage, conversationHistory, customBasePrompt);
+
+      // Create context and append the prompt as a user message
+      // (Using direct append since the feed generator prompt is built dynamically)
+      const ctx = AiDelegate.createContext();
+      ctx.append('user', promptContent);
+
+      // Complete the context with the AI
+      await AiDelegate.completeContext(ctx, {
         weight,
         max_tokens: 3000,
-        temperature: 0,
-        messages: [{ role: 'user', content: prompt }],
-      })) as AiResponse;
+        temperature: 0
+      });
 
-      const responseContent = response.content.trim();
+      const responseContent = (ctx.last as string).trim();
 
       // Extract SQL from markdown code block
       // Check for prompt modification response first
diff --git a/ratings-ui-demo/src/services/prompts.ts b/ratings-ui-demo/src/services/prompts.ts
new file mode 100644
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+/**
+ * AI Prompt Definitions for PeerVerity Ratings
+ *
+ * This module defines named prompts for the ai-delegate prompt management system.
+ * Prompts are registered at startup and can be customized by users via the Prompt Editor UI.
+ */
+
+import type { PromptRegistration } from '@peerverity/ai-delegate';
+
+/**
+ * Get database schema context for SQL generation.
+ * This is dynamic content that gets passed as a parameter to prompts.
+ */
+export function getDatabaseSchemaContext(): string {
+  return `
+PeerVerity Database Context:
+PeerVerity is a system for rating the truth value of propositions (claims/statements) on a scale from 0 (false) to 1 (true).
+Users can rate propositions, create arguments (inter-proposition links), and delegate voting power to experts.
+
+CRITICAL DATABASE SCHEMA - PAY ATTENTION TO COLUMN NAMES:
+
+Table: users
+- id (integer, primary key): Unique user ID
+- username (text): The user's username - THIS is the user identifier, NOT 'name'
+- kind (text): Type of user ('person' for humans, 'algorithm' for system users, 'organization')
+- email_alert_frequency (interval): Email alert frequency
+- creation_time (timestamp): When the user was created
+- creator_id (integer): ID of user who created this user (relevant for algorithm users)
+- initweight (numeric): Initial weight for delegation algorithms (algorithm users only)
+- hilimfac (numeric): High limit factor for delegation algorithms (algorithm users only)
+- lolim (numeric): Low limit for delegation algorithms (algorithm users only)
+
+Table: propositions
+- id (integer, primary key): Unique proposition ID
+- body (text): **THE ACTUAL PROPOSITION TEXT IS STORED HERE** - NOT in a column called 'proposition'!
+- creation_time (timestamp): When the proposition was created
+- creator_id (integer, foreign key to users.id): User who created the proposition
+
+Table: ratings
+- id (integer, primary key): Unique rating ID
+- proposition_id (integer, foreign key to propositions.id): Proposition being rated
+- rating (numeric): Rating value between 0 (false) and 1 (true)
+- creation_time (timestamp): When the rating was created
+- creator_id (integer, foreign key to users.id): User who created the rating
+
+Table: latest_valid_ratings (view) - USE THIS INSTEAD OF ratings TABLE
+- proposition_id (integer): Proposition ID
+- rating (numeric): Rating value (0=false, 1=true)
+- creator_id (integer): User ID who created the rating
+- creation_time (timestamp): When the rating was created
+Note: This view handles superseded ratings - users can update their ratings and this shows only the latest
+
+Table: documents
+- id (integer, primary key): Unique document ID
+- title (text): Document title
+- body (bytea): Document content (binary data)
+- creation_time (timestamp): When the document was created
+- creator_id (integer, foreign key to users.id): User who created the document
+
+Table: tags
+- id (integer, primary key): Unique tag ID
+- name (text): The tag name - THIS is the tag text, NOT 'tag'
+- useful_proposition_id (integer): Related proposition for tag usefulness
+- creation_time (timestamp): When the tag was created
+- creator_id (integer, foreign key to users.id): User who created the tag
+
+Table: delegations
+- id (integer, primary key): Unique delegation ID
+- creator_id (integer, foreign key to users.id): User delegating their vote
+- receiver_id (integer, foreign key to users.id): User receiving the delegation
+- tag_id (integer, foreign key to tags.id): Tag for which delegation applies
+- weight (numeric): Weight of the delegation
+- creation_time (timestamp): When the delegation was created
+
+Table: inter_proposition_links (arguments connecting propositions)
+- id (integer, primary key): Unique link ID
+- target_id (integer): Target proposition being supported/opposed
+- origin_id (integer): Origin proposition providing the argument
+- link_type (text): Type of link ('supports', 'opposes', 'rewording')
+- impact_proposition_id (integer): Proposition rating the impact of this argument
+- creator_id (integer): User who created the link
+- creation_time (timestamp): When created
+
+Table: document_proposition_links
+- proposition_id (integer): Proposition being supported by document
+- document_id (integer): Supporting document
+- relevancy_proposition_id (integer): Proposition rating the relevancy
+- creator_id (integer): User who created the link
+- creation_time (timestamp): When created
+
+Table: sources (original information sources)
+- id (integer, primary key): Unique source ID
+- name (text): Source name
+- kind (text): Type ('person', 'ai', 'organization', 'bot', 'other')
+- url (text): Source URL
+- creator_id (integer): User who created this source
+- creation_time (timestamp): When created
+
+Table: cached_aggregate_ratings (pre-computed aggregate ratings per algorithm)
+- id (integer, primary key): Unique record ID
+- proposition_id (integer, foreign key to propositions.id): The proposition this rating is for
+- creator_id (integer, foreign key to users.id): **The algorithm's user account** - links to rating_calculation_procedures
+- aggregate_rating (numeric): The calculated rating value (0 = false, 1 = true)
+- number_of_ratings (integer): Count of individual ratings used in calculation
+- weight_of_ratings (numeric): Sum of weights used in calculation
+- median_stat, mode_stat, stddev_stat, var_stat, min_stat, max_stat, range_stat, q1_stat, q3_stat, iqr_stat (numeric): Statistical values
+
+Table: rating_calculation_procedures (rating algorithms)
+- id (integer, primary key): Algorithm ID
+- name (text): Algorithm name displayed to users ('Mean', 'Median', 'Statistics', etc.)
+- creator_id (integer, foreign key to users.id): **The algorithm's user account** - use this to join with cached_aggregate_ratings
+- procedure_name (text): Internal procedure name
+- is_enabled (boolean): Whether the algorithm is active
+- description (text): Algorithm description
+
+CRITICAL JOIN PATTERN for aggregate ratings by algorithm:
+SELECT car.aggregate_rating
+FROM cached_aggregate_ratings car
+JOIN rating_calculation_procedures rcp ON car.creator_id = rcp.creator_id
+WHERE rcp.name = 'Mean'  -- Use 'Mean', 'Median', 'Statistics', etc.
+
+COMMON MISTAKES TO AVOID:
+- WRONG: WHERE p.proposition = 'some text'
+- RIGHT: WHERE p.body = 'some text'
+
+- WRONG: WHERE u.name = 'john'
+- RIGHT: WHERE u.username = 'john'
+
+- WRONG: WHERE t.tag = 'economics'
+- RIGHT: WHERE t.name = 'economics'
+
+- WRONG: JOIN calculated_ratings cr ON cr.proposition_id = p.id (table doesn't exist!)
+- RIGHT: JOIN cached_aggregate_ratings car ON car.proposition_id = p.id
+          JOIN rating_calculation_procedures rcp ON car.creator_id = rcp.creator_id
+
+- WRONG: WHERE cr.algorithm = 'mean' (no 'algorithm' column exists)
+- RIGHT: WHERE rcp.name = 'Mean' (use rating_calculation_procedures.name)
+
+CORRECT SQL EXAMPLES:
+-- Finding users who rated a specific proposition by its text:
+SELECT DISTINCT lvr.creator_id AS user_id
+FROM latest_valid_ratings lvr
+JOIN propositions p ON p.id = lvr.proposition_id
+WHERE p.body = 'The earth is round';  -- Use 'body' column for proposition text!
+
+-- Finding propositions containing specific words:
+SELECT id AS proposition_id
+FROM propositions
+WHERE body ILIKE '%climate%';  -- Use 'body' for text search!
+
+-- Finding human users only (not algorithms):
+SELECT id AS user_id
+FROM users
+WHERE kind = 'person';  -- Use exact value 'person' not 'human'
+
+-- Finding users who delegated on a specific tag:
+SELECT DISTINCT d.creator_id AS user_id
+FROM delegations d
+JOIN tags t ON t.id = d.tag_id
+WHERE t.name = 'economics';  -- Use 'name' column for tag text!
+
+QUERY RULES:
+1. ALWAYS return a column aliased as 'user_id' for user selection queries
+2. Use latest_valid_ratings view, NOT the ratings table directly
+3. Proposition text is in propositions.body column
+4. Username is in users.username column
+5. Tag text is in tags.name column
+6. User kinds are: 'person' (humans), 'algorithm' (system users), 'organization'
+7. Ratings are 0 (false) to 1 (true), where 0.5 is uncertain
+`;
+}
+
+/**
+ * Get extended schema context with additional tables for analysis queries
+ */
+export function getExtendedSchemaContext(): string {
+  return `
+ADDITIONAL TABLES AND VIEWS FOR ANALYSIS:
+
+IMPORTANT: Override the default instruction about latest_valid_ratings:
+- Use the 'ratings' table for historical analysis, time-series, and rating history
+- Only use 'latest_valid_ratings' view when you specifically need just the most recent rating
+
+Table: ratings (use this for historical analysis!)
+- id (integer): Rating ID
+- creator_id (integer): User who created the rating
+- creation_time (timestamp): When the rating was created
+- proposition_id (integer): The proposition being rated
+- rating (numeric): Rating value (0 = false, 1 = true)
+
+Table: rating_calculation_procedures (rating algorithms)
+- id (integer): Algorithm ID
+- name (text): Algorithm name displayed to users ('Mean', 'Median', 'Statistics', etc.)
+- creator_id (integer): **The algorithm's user account** - use this to join with cached_aggregate_ratings
+- procedure_name (text): Internal procedure name
+- is_enabled (boolean): Whether the algorithm is active
+- description (text): Algorithm description
+
+Table: cached_aggregate_ratings (aggregated ratings per algorithm)
+- proposition_id (integer): The proposition this rating is for
+- creator_id (integer): **The algorithm's user ID** - join to rating_calculation_procedures.creator_id
+- aggregate_rating (numeric): Calculated rating value (0 to 1)
+- number_of_ratings (integer): Number of ratings used
+- weight_of_ratings (numeric): Sum of weights used
+- median_stat, mode_stat, stddev_stat, var_stat, min_stat, max_stat, range_stat, q1_stat, q3_stat, iqr_stat (numeric): Statistical values
+
+Table: delegation_weights (calculated delegation weights)
+- delegator_id (integer): User delegating
+- receiver_id (integer): User receiving delegation
+- tag_id (integer): Tag for delegation
+- effective_weight (numeric): Calculated weight
+`;
+}
+
+/**
+ * Get Python/plpython3u integration context for analysis prompts
+ */
+export function getPythonIntegrationContext(): string {
+  return `
+PYTHON INTEGRATION (plpython3u):
+PostgreSQL has plpython3u enabled, allowing Python functions for complex calculations.
+
+IMPORTANT: If the user explicitly mentions "Python", "in Python", "using Python",
+"with Python", "plpython3u", etc., you MUST use Python integration even if SQL alone could work.
+User preference for implementation method always takes priority.
+
+When to use Python:
+1. User explicitly requests it (ALWAYS honor this - highest priority)
+2. Complex mathematical operations (statistics, custom algorithms, advanced rounding)
+3. Data transformations that are clearer in Python than SQL
+4. Multi-step calculations easier to express in procedural code
+
+Pattern for Python functions:
+CREATE OR REPLACE FUNCTION function_name(param1 type1, param2 type2)
+RETURNS return_type AS $$
+    # Section 1: Extract data from database (if needed)
+    query = f"SELECT column FROM table WHERE id = {{param1}}"
+    result = plpy.execute(query)
+    data = [row['column'] for row in result]
+
+    # Section 2: Python calculations
+    calculated_value = your_python_logic(data)
+
+    # Section 3: Return result
+    return calculated_value
+$$ LANGUAGE plpython3u;
+
+Simple example - Rounding to tenth:
+CREATE OR REPLACE FUNCTION round_to_tenth(value numeric)
+RETURNS numeric AS $$
+    import decimal
+    val = float(value)
+    rounded = round(val, 1)  # Round to 1 decimal place
+    return decimal.Decimal(str(rounded))
+$$ LANGUAGE plpython3u;
+
+Usage in query:
+SELECT proposition_id, round_to_tenth(aggregate_rating) AS rounded_rating
+FROM cached_aggregate_ratings;
+
+For more complex examples, see the database functions:
+- python_rating_algorithm(creator_id, proposition_id) - median calculation
+- python_statistics_algorithm(creator_id, proposition_id) - comprehensive statistics
+`;
+}
+
+/**
+ * All prompt registrations for the ratings application
+ */
+export const promptRegistrations: PromptRegistration[] = [
+  // User Selection Query Prompt
+  {
+    name: 'user_selection_query',
+    version: '1.0.0',
+    prompt: {
+      template: `You are a SQL query generator for selecting users from the PeerVerity database.
+Generate a SELECT query that returns user IDs based on the natural language description provided.
+
+{schemaContext}
+
+CRITICAL RULES FOR USER SELECTION QUERIES:
+1. The query MUST return exactly one column aliased as 'user_id'
+2. Use DISTINCT to avoid duplicate user IDs
+3. Only SELECT statements are allowed
+4. Use latest_valid_ratings view instead of ratings table for current ratings
+5. No semicolon at the end of the query
+
+EXAMPLES:
+-- Users who rated a specific proposition above a threshold:
+SELECT DISTINCT lvr.creator_id AS user_id
+FROM latest_valid_ratings lvr
+WHERE lvr.proposition_id = 63 AND lvr.rating > 0.7
+
+-- Users who delegated on a specific tag:
+SELECT DISTINCT d.creator_id AS user_id
+FROM delegations d
+JOIN tags t ON t.id = d.tag_id
+WHERE t.name = 'economics'
+
+-- Human users only (excluding algorithms):
+SELECT id AS user_id
+FROM users
+WHERE kind = 'person'
+
+-- Users who created propositions containing specific text:
+SELECT DISTINCT p.creator_id AS user_id
+FROM propositions p
+WHERE p.body ILIKE '%climate%'
+
+User request: {naturalLanguage}
+
+Generate the SQL query (return ONLY the SQL, no explanations):`,
+      params: {
+        schemaContext: { help: 'Database schema context' },
+        naturalLanguage: { help: 'Natural language description of user selection criteria' }
+      },
+      role: 'user',
+      weight: 'moderate'
+    }
+  },
+
+  // Analysis Query Prompt
+  {
+    name: 'analysis_query',
+    version: '1.0.0',
+    prompt: {
+      template: `You are a SQL query generator for PeerVerity database analysis and reporting.
+Generate a SELECT query based on the natural language description provided.
+
+{schemaContext}
+
+{extendedSchemaContext}
+
+CRITICAL INSTRUCTIONS:
+1. Generate flexible SELECT queries that return data for analysis
+2. Include meaningful column aliases for display
+3. Use JOINs to include related data when helpful
+4. Support time-based queries with date functions
+5. Allow aggregations (COUNT, AVG, SUM, etc.)
+6. Include ORDER BY for meaningful default sorting
+7. Do not limit results unless specifically requested
+8. Return columns with clear, descriptive aliases
+9. Ensure queries are valid PostgreSQL syntax
+10. No semicolon at the end
+11. IMPORTANT: When aggregating from multiple one-to-many relationships, use subqueries to aggregate each relationship separately to avoid Cartesian products
+12. CRITICAL: Always qualify ALL column names with table aliases to avoid ambiguity errors
+13. IMPORTANT: Return ONLY the SQL query, no explanatory text
+
+EXAMPLES OF ANALYSIS QUERIES:
+
+-- Ratings over time for a specific user and proposition
+SELECT
+    DATE(r.creation_time) AS date,
+    r.rating AS rating_value,
+    p.body AS proposition_text
+FROM latest_valid_ratings r
+JOIN propositions p ON p.id = r.proposition_id
+WHERE r.creator_id = 15 AND r.proposition_id = 42
+ORDER BY r.creation_time
+
+-- Top influential voters by tag
+SELECT
+    u.username,
+    t.name AS tag_name,
+    SUM(dw.effective_weight) AS total_influence
+FROM delegation_weights dw
+JOIN users u ON u.id = dw.receiver_id
+JOIN tags t ON t.id = dw.tag_id
+GROUP BY u.username, t.name
+ORDER BY total_influence DESC
+LIMIT 10
+
+-- Algorithm comparison for propositions
+SELECT
+    p.id AS proposition_id,
+    p.body AS proposition,
+    MAX(CASE WHEN rcp.name = 'Mean' THEN car.aggregate_rating END) AS mean_rating,
+    MAX(CASE WHEN rcp.name = 'Median' THEN car.aggregate_rating END) AS median_rating,
+    MAX(CASE WHEN rcp.name = 'ComRating3' THEN car.aggregate_rating END) AS comrating3_rating
+FROM propositions p
+LEFT JOIN cached_aggregate_ratings car ON car.proposition_id = p.id
+LEFT JOIN rating_calculation_procedures rcp ON car.creator_id = rcp.creator_id
+GROUP BY p.id, p.body
+ORDER BY p.id
+
+User request: {naturalLanguage}
+
+Generate the SQL query:`,
+      params: {
+        schemaContext: { help: 'Database schema context' },
+        extendedSchemaContext: { help: 'Extended schema context for analysis' },
+        naturalLanguage: { help: 'Natural language description of the analysis' }
+      },
+      role: 'user',
+      weight: 'heavy'
+    }
+  },
+
+  // Analyze Data with Results (Phase 2)
+  {
+    name: 'analyze_data_with_results',
+    version: '1.0.0',
+    prompt: {
+      template: `You are a conversational data analyst with access to the PeerVerity database.
+
+DATABASE SCHEMA:
+{schemaContext}
+
+{conversationContext}
+
+{tableContext}
+
+CURRENT USER QUESTION: {naturalLanguage}
+
+{pythonIntegrationContext}
+
+INSTRUCTIONS FOR CONVERSATIONAL ANALYSIS:
+1. This is part of an ongoing conversation - reference previous context when relevant
+2. The data shown above is from a previous SQL query in this conversation
+3. ONLY describe what you see in the actual data provided
+4. When users ask for "all", "every", "list each" - provide COMPLETE lists, don't summarize
+5. Only summarize if listing more than 50 items (and clearly state this)
+6. DO NOT make up or imagine examples that aren't in the data
+7. DO NOT hallucinate counter-arguments or content not present in the results
+8. When mentioning specific items, QUOTE EXACTLY from the data
+9. If the data shows counter-arguments, list ONLY the ones actually present in the results
+10. Use phrases like "The data shows..." or "According to the results..." to ground your response
+11. If something isn't in the provided data, say "The data doesn't show..." or "Not found in these results"
+12. Build upon previous insights if this is a follow-up question
+13. If asked about something not in the current data, you MUST generate a new SQL query
+14. Be conversational but precise - quote exact values from the data
+15. CRITICAL: All SQL queries are executed AUTOMATICALLY - never ask for permission
+16. NEVER ask "Would you like me to execute?" or similar questions
+17. If you need more data, include it as a SQL code block:
+    \`\`\`sql
+    [YOUR SQL HERE]
+    \`\`\`
+18. For rating-related queries:
+    - Use 'ratings' table for: history, time-series, changes over time
+    - Use 'latest_valid_ratings' view ONLY for: current state, most recent rating
+19. When user requests Python calculations, include both CREATE and SELECT statements
+
+Respond naturally as part of the conversation.`,
+      params: {
+        schemaContext: { help: 'Database schema context' },
+        conversationContext: { help: 'Previous conversation history' },
+        tableContext: { help: 'Current table data from previous query' },
+        naturalLanguage: { help: 'Current user question' },
+        pythonIntegrationContext: { help: 'Python/plpython3u integration context' }
+      },
+      role: 'user',
+      weight: 'heavy'
+    }
+  },
+
+  // Analyze Data Initial (Phase 1 - no data yet)
+  {
+    name: 'analyze_data_initial',
+    version: '1.0.0',
+    prompt: {
+      template: `You are a conversational data analyst with FULL ACCESS to the PeerVerity database.
+
+DATABASE SCHEMA:
+{schemaContext}
+
+{conversationContext}
+
+CURRENT USER QUESTION: {naturalLanguage}
+
+{pythonIntegrationContext}
+
+INSTRUCTIONS:
+1. This is {conversationStatus} a conversation
+2. IMPORTANT: Check if you can answer from the conversation history BEFORE generating SQL
+3. If the user references "these", "those", "the above" - they're referring to previous results
+4. Only generate SQL if you need NEW data not already in the conversation
+5. If the user asks to filter, categorize, or analyze data already shown, answer directly without SQL
+6. You have complete access to the PeerVerity database when new data is needed
+7. Generate SQL when asked about NEW specific content not already discussed
+8. Exploratory questions about NEW topics need SQL to provide real answers
+9. Reference previous conversation context when relevant
+10. CRITICAL: All SQL queries are executed AUTOMATICALLY - never ask for permission
+11. NEVER ask "Would you like me to execute?" or "Shall I run this query?"
+12. For counter-arguments: Use inter_proposition_links table with link_type = 'opposes'
+13. IMPORTANT for rating queries:
+    - Use 'ratings' table for: history, time-series, changes over time
+    - Use 'latest_valid_ratings' view ONLY for: current state, most recent rating
+14. IMPORTANT for UNION queries: All columns must have matching types
+
+Please respond in JSON format:
+{{
+    "needs_sql": true/false,
+    "sql_query": "SELECT ... (REQUIRED for any question about specific data/content/users)",
+    "answer_preview": "When SQL is needed, include it like: 'I'll retrieve that information using this SQL query:\\n\`\`\`sql\\n[SQL HERE]\\n\`\`\`\\n\\nExecuting the query now...' Otherwise just provide the answer.",
+    "references_previous": true/false (does this build on previous conversation?)
+}}
+
+Examples of questions that MUST have needs_sql=true:
+- "Tell me about the socialism discussion" (new topic not discussed)
+- "What has alice rated?" (new query about user)
+- "Summarize the database content" (exploratory new query)
+
+Examples of questions that should have needs_sql=false:
+- "Of these 34 propositions, which are about animals?" (filtering previous results)
+- "What was the highest rating you mentioned?" (referencing previous answer)
+- "Can you categorize those users by type?" (analyzing shown data)`,
+      params: {
+        schemaContext: { help: 'Database schema context' },
+        conversationContext: { help: 'Previous conversation history' },
+        naturalLanguage: { help: 'Current user question' },
+        pythonIntegrationContext: { help: 'Python/plpython3u integration context' },
+        conversationStatus: { help: '"the start of" or "a continuation of"', default: 'the start of' }
+      },
+      role: 'user',
+      weight: 'heavy'
+    }
+  },
+
+  // Context Follow-up Prompt
+  {
+    name: 'analyze_context_followup',
+    version: '1.0.0',
+    prompt: {
+      template: `You are analyzing a conversation and need to answer based on previous context.
+
+PREVIOUS CONVERSATION:
+{conversationContext}
+
+CURRENT QUESTION: {naturalLanguage}
+
+INSTRUCTIONS:
+1. Answer based ONLY on information from the previous conversation
+2. When asked to list items, provide the COMPLETE list - don't summarize
+3. If asked for "every" or "all" items, list them explicitly
+4. Only summarize if there are more than 50 items (and state this)
+5. Be specific and comprehensive in your response
+6. If filtering or categorizing, show the exact items that match
+
+Provide the complete answer based on the conversation history.`,
+      params: {
+        conversationContext: { help: 'Previous conversation history' },
+        naturalLanguage: { help: 'Current user question' }
+      },
+      role: 'user',
+      weight: 'heavy'
+    }
+  },
+
+  // Event Query Prompt
+  {
+    name: 'event_query',
+    version: '1.0.0',
+    prompt: {
+      template: `You are an AI assistant that helps users create custom event detection queries for the PeerVerity system.
+Users want to receive notifications when certain conditions are met in the database.
+
+{schemaContext}
+
+ADDITIONAL CONTEXT FOR EVENT QUERIES:
+
+Table: followed_propositions
+- id (integer): Follow record ID
+- creator_id (integer): User who is following
+- proposition_id (integer): Proposition being followed
+- event_types (text[]): Types of events to track
+- creation_time (timestamp): When follow was created
+
+Table: followed_tags, followed_documents (similar structure)
+
+EVENT QUERY CONTRACT:
+The SQL query should return rows that represent entities currently meeting the event condition.
+Each row returned means "this entity currently triggers the event."
+
+Required output columns:
+- entity_type (text): The type of entity (e.g., 'proposition', 'tag', 'user', 'document')
+- entity_id (integer): The ID of the entity
+- display_text (text): A human-readable description for the notification feed
+
+EXAMPLES:
+
+-- Event: Proposition receives more than 10 ratings in the last hour
+SELECT
+    'proposition'::text AS entity_type,
+    p.id AS entity_id,
+    'Proposition "' || LEFT(p.body, 50) || '..." received more than 10 ratings in the last hour' AS display_text
+FROM propositions p
+WHERE (
+    SELECT COUNT(*)
+    FROM ratings r
+    WHERE r.proposition_id = p.id
+    AND r.creation_time > NOW() - INTERVAL '1 hour'
+) > 10;
+
+-- Event: Proposition aggregate rating exceeds threshold
+SELECT
+    'proposition'::text AS entity_type,
+    p.id AS entity_id,
+    'Proposition "' || LEFT(p.body, 50) || '..." reached ' || ROUND(car.aggregate_rating * 100) || '% rating' AS display_text
+FROM propositions p
+JOIN cached_aggregate_ratings car ON car.proposition_id = p.id
+JOIN rating_calculation_procedures rcp ON car.creator_id = rcp.creator_id
+WHERE rcp.name = 'Mean' AND car.aggregate_rating > 0.8;
+
+{conversationContext}
+
+User request: {naturalLanguage}
+
+Generate a SQL query that detects this event condition. Return ONLY the SQL query, followed by a brief explanation.
+Format your response as:
+\`\`\`sql
+<your SQL query here>
+\`\`\`
+
+Explanation: <brief explanation of what it does>`,
+      params: {
+        schemaContext: { help: 'Database schema context' },
+        conversationContext: { help: 'Previous conversation for context', default: '' },
+        naturalLanguage: { help: 'Natural language description of the event to detect' }
+      },
+      role: 'user',
+      weight: 'heavy'
+    }
+  },
+
+  // Feed Generator Query Prompt
+  {
+    name: 'feed_generator_query',
+    version: '1.0.0',
+    prompt: {
+      template: `You are an AI assistant that helps users create feed generators for the PeerVerity system.
+Feed generators produce events that appear in users' activity feeds.
+
+{schemaContext}
+
+FEED GENERATOR CONTRACT:
+The SQL query MUST return exactly these 8 columns:
+1. event_time (TIMESTAMP) - When the event occurred
+2. event_type (VARCHAR) - A unique identifier for the event type
+3. primary_entity_type (VARCHAR) - The main entity type ('proposition', 'document', 'user', 'tag', 'source')
+4. primary_entity_id (INTEGER) - ID of the primary entity
+5. secondary_entity_type (VARCHAR) - Optional related entity type (can be NULL::VARCHAR)
+6. secondary_entity_id (INTEGER) - Optional related entity ID (can be NULL::INTEGER)
+7. actor_user_id (INTEGER) - The user who triggered the event
+8. event_data (JSONB) - Custom data for the MDX template (use jsonb_build_object())
+
+MDX TEMPLATE VARIABLES:
+- {{data.eventTime}} - ISO timestamp string
+- {{new Date(data.eventTime).toLocaleString()}} - Formatted date/time
+- {{data.eventType}} - The event type string
+- {{data.primaryEntityType}}, {{data.primaryEntityId}} - Primary entity info
+- {{data.secondaryEntityType}}, {{data.secondaryEntityId}} - Secondary entity info
+- {{data.actorUserId}} - Actor user ID
+- {{data.eventData.fieldName}} - Access custom fields from event_data JSONB
+
+MDX TEMPLATE BEST PRACTICES:
+All MDX templates MUST follow this exact 2-line format:
+Line 1: **Event Type Title** - {{new Date(data.eventTime).toLocaleString()}}
+Line 2: <single sentence with linked entities and bolded key values>
+
+STANDARD LINK PATTERNS (use JSX <a> tags, NOT markdown):
+- Users: <a href={{'/pages/user?username=' + data.eventData.username}}>{{data.eventData.username}}</a>
+- Propositions: <a href={{'/pages/proposition?proposition_id=' + data.primaryEntityId}}>{{data.eventData.body}}</a>
+
+CRITICAL: EACH ROW = ONE FEED EVENT
+- Use DISTINCT ON, LIMIT 1, or window functions to avoid duplicates
+- The event_time should be the timestamp of the ACTION that triggered the event
+- The actor_user_id should be the user who performed that action
+
+EXAMPLE - New Propositions:
+\`\`\`sql
+SELECT
+    p.creation_time AS event_time,
+    'proposition_created' AS event_type,
+    'proposition' AS primary_entity_type,
+    p.id AS primary_entity_id,
+    NULL::VARCHAR AS secondary_entity_type,
+    NULL::INTEGER AS secondary_entity_id,
+    p.creator_id AS actor_user_id,
+    jsonb_build_object(
+        'body', p.body,
+        'creator_username', u.username
+    ) AS event_data
+FROM public.propositions p
+JOIN public.users u ON u.id = p.creator_id
+ORDER BY p.creation_time DESC
+\`\`\`
+
+\`\`\`mdx
+**Proposition Created** - {{new Date(data.eventTime).toLocaleString()}}
+
+<a href={{'/pages/user?username=' + data.eventData.creator_username}}>{{data.eventData.creator_username}}</a> created <a href={{'/pages/proposition?proposition_id=' + data.primaryEntityId}}>{{data.eventData.body}}</a>
+\`\`\`
+
+{customBasePrompt}
+
+{conversationContext}
+
+User request: {naturalLanguage}
+
+INSTRUCTIONS:
+- If this is a PROMPT CUSTOMIZATION request: Output ONLY a \`\`\`prompt_customization block
+- If this is a FEED GENERATOR request: Output BOTH \`\`\`sql and \`\`\`mdx blocks
+
+Format for feed generator:
+\`\`\`sql
+<SQL query here>
+\`\`\`
+
+\`\`\`mdx
+<MDX template here>
+\`\`\`
+
+Explanation: <brief description>`,
+      params: {
+        schemaContext: { help: 'Database schema context' },
+        conversationContext: { help: 'Previous conversation for context', default: '' },
+        naturalLanguage: { help: 'Natural language description of the feed generator' },
+        customBasePrompt: { help: 'User customizations to append to base prompt', default: '' }
+      },
+      role: 'user',
+      weight: 'heavy'
+    }
+  }
+];
+
+/**
+ * Get prompt registration for a specific prompt name
+ */
+export function getPromptRegistration(name: string): PromptRegistration | undefined {
+  return promptRegistrations.find(p => p.name === name);
+}