Wiki source code of POC Summary (POC1 & POC2)
Last modified by Robert Schaub on 2025/12/24 21:53
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2.1 | 1 | = POC Summary (POC1 & POC2) = |
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1.1 | 2 | |
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2.1 | 4 | {{info}} |
| 5 | **This page describes POC1 v0.4+ (3-stage pipeline with caching).** | ||
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1.1 | 6 | |
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2.1 | 7 | For complete implementation details, see [[POC1 API & Schemas Specification>>FactHarbor.Specification.POC.API-and-Schemas.WebHome]]. |
| 8 | {{/info}} | ||
| 9 | |||
| 10 | |||
| 11 | |||
| 12 | == 1. POC Specification == | ||
| 13 | |||
| 14 | === POC Goal | ||
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1.1 | 15 | Prove that AI can extract claims and determine verdicts automatically without human intervention. |
| 16 | |||
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2.1 | 17 | === POC Output (4 Components Only) |
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1.1 | 18 | |
| 19 | **1. ANALYSIS SUMMARY** | ||
| 20 | - 3-5 sentences | ||
| 21 | - How many claims found | ||
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2.1 | 22 | - Distribution of verdicts |
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1.1 | 23 | - Overall assessment |
| 24 | |||
| 25 | **2. CLAIMS IDENTIFICATION** | ||
| 26 | - 3-5 numbered factual claims | ||
| 27 | - Extracted automatically by AI | ||
| 28 | |||
| 29 | **3. CLAIMS VERDICTS** | ||
| 30 | - Per claim: Verdict label + Confidence % + Brief reasoning (1-3 sentences) | ||
| 31 | - Verdict labels: WELL-SUPPORTED / PARTIALLY SUPPORTED / UNCERTAIN / REFUTED | ||
| 32 | |||
| 33 | **4. ARTICLE SUMMARY (optional)** | ||
| 34 | - 3-5 sentences | ||
| 35 | - Neutral summary of article content | ||
| 36 | |||
| 37 | **Total output: ~200-300 words** | ||
| 38 | |||
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2.1 | 39 | === What's NOT in POC |
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1.1 | 40 | |
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2.1 | 41 | ❌ Scenarios (multiple interpretations) |
| 42 | ❌ Evidence display (supporting/opposing lists) | ||
| 43 | ❌ Source links | ||
| 44 | ❌ Detailed reasoning chains | ||
| 45 | ❌ User accounts, history, search | ||
| 46 | ❌ Browser extensions, API | ||
| 47 | ❌ Accessibility, multilingual, mobile | ||
| 48 | ❌ Export, sharing features | ||
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1.1 | 49 | ❌ Any other features |
| 50 | |||
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2.1 | 51 | === Critical Requirement |
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1.1 | 52 | |
| 53 | **FULLY AUTOMATED - NO MANUAL EDITING** | ||
| 54 | |||
| 55 | This is non-negotiable. POC tests whether AI can do this without human intervention. | ||
| 56 | |||
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2.1 | 57 | === POC Success Criteria |
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1.1 | 58 | |
| 59 | **Passes if:** | ||
| 60 | - ✅ AI extracts 3-5 factual claims automatically | ||
| 61 | - ✅ AI provides reasonable verdicts (≥70% make sense) | ||
| 62 | - ✅ Output is comprehensible | ||
| 63 | - ✅ Team agrees approach has merit | ||
| 64 | - ✅ Minimal or no manual editing needed | ||
| 65 | |||
| 66 | **Fails if:** | ||
| 67 | - ❌ Claim extraction poor (< 60% accuracy) | ||
| 68 | - ❌ Verdicts nonsensical (< 60% reasonable) | ||
| 69 | - ❌ Requires manual editing for most analyses (> 50%) | ||
| 70 | - ❌ Team loses confidence in approach | ||
| 71 | |||
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2.1 | 72 | === POC Architecture |
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1.1 | 73 | |
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2.1 | 74 | **Frontend:** Simple input form + results display |
| 75 | **Backend:** Single API call to Claude (Sonnet 4.5) | ||
| 76 | **Processing:** One prompt generates complete analysis | ||
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1.1 | 77 | **Database:** None required (stateless) |
| 78 | |||
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2.1 | 79 | === POC Philosophy |
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1.1 | 80 | |
| 81 | > "Build less, learn more, decide faster. Test the hardest part first." | ||
| 82 | |||
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2.1 | 83 | === Context-Aware Analysis (Experimental POC1 Feature) === |
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1.1 | 84 | |
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2.1 | 85 | **Problem:** Article credibility ≠ simple average of claim verdicts |
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1.1 | 86 | |
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2.1 | 87 | **Example:** Article with accurate facts (coffee has antioxidants, antioxidants fight cancer) but false conclusion (therefore coffee cures cancer) would score as "mostly accurate" with simple averaging, but is actually MISLEADING. |
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1.1 | 88 | |
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2.1 | 89 | **Solution (POC1 Test):** Approach 1 - Single-Pass Holistic Analysis |
| 90 | * Enhanced AI prompt to evaluate logical structure | ||
| 91 | * AI identifies main argument and assesses if it follows from evidence | ||
| 92 | * Article verdict may differ from claim average | ||
| 93 | * Zero additional cost, no architecture changes | ||
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1.1 | 94 | |
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2.1 | 95 | **Testing:** |
| 96 | * 30-article test set | ||
| 97 | * Success: ≥70% accuracy detecting misleading articles | ||
| 98 | * Marked as experimental | ||
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1.1 | 99 | |
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2.1 | 100 | **See:** [[Article Verdict Problem>>FactHarbor.Specification.POC.Article-Verdict-Problem]] for full analysis and solution approaches. |
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1.1 | 101 | |
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2.1 | 102 | == 2. POC2 Specification == |
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1.1 | 103 | |
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2.1 | 104 | === POC2 Goal === |
| 105 | Prove that AKEL produces high-quality outputs consistently at scale with complete quality validation. | ||
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1.1 | 106 | |
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2.1 | 107 | === POC2 Enhancements (From POC1) === |
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1.1 | 108 | |
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2.1 | 109 | **1. COMPLETE QUALITY GATES (All 4)** |
| 110 | * Gate 1: Claim Validation (from POC1) | ||
| 111 | * Gate 2: Evidence Relevance ← NEW | ||
| 112 | * Gate 3: Scenario Coherence ← NEW | ||
| 113 | * Gate 4: Verdict Confidence (from POC1) | ||
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1.1 | 114 | |
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2.1 | 115 | **2. EVIDENCE DEDUPLICATION (FR54)** |
| 116 | * Prevent counting same source multiple times | ||
| 117 | * Handle syndicated content (AP, Reuters) | ||
| 118 | * Content fingerprinting with fuzzy matching | ||
| 119 | * Target: >95% duplicate detection accuracy | ||
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1.1 | 120 | |
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2.1 | 121 | **3. CONTEXT-AWARE ANALYSIS (Conditional)** |
| 122 | * **If POC1 succeeds (≥70%):** Implement as standard feature | ||
| 123 | * **If POC1 promising (50-70%):** Try weighted aggregation approach | ||
| 124 | * **If POC1 fails (<50%):** Defer to post-POC2 | ||
| 125 | * Detects articles with accurate claims but misleading conclusions | ||
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1.1 | 126 | |
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2.1 | 127 | **4. QUALITY METRICS DASHBOARD (NFR13)** |
| 128 | * Track hallucination rates | ||
| 129 | * Monitor gate performance | ||
| 130 | * Evidence quality metrics | ||
| 131 | * Processing statistics | ||
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1.1 | 132 | |
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2.1 | 133 | === What's Still NOT in POC2 === |
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1.1 | 134 | |
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2.1 | 135 | ❌ User accounts, authentication |
| 136 | ❌ Public publishing interface | ||
| 137 | ❌ Social sharing features | ||
| 138 | ❌ Full production security (comes in Beta 0) | ||
| 139 | ❌ In-article claim highlighting (comes in Beta 0) | ||
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1.1 | 140 | |
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2.1 | 141 | === Success Criteria === |
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1.1 | 142 | |
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2.1 | 143 | **Quality:** |
| 144 | * Hallucination rate <5% (target: <3%) | ||
| 145 | * Average quality rating ≥8.0/10 | ||
| 146 | * Gates identify >95% of low-quality outputs | ||
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1.1 | 147 | |
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2.1 | 148 | **Performance:** |
| 149 | * All 4 quality gates operational | ||
| 150 | * Evidence deduplication >95% accurate | ||
| 151 | * Quality metrics tracked continuously | ||
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1.1 | 152 | |
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2.1 | 153 | **Context-Aware (if implemented):** |
| 154 | * Maintains ≥70% accuracy detecting misleading articles | ||
| 155 | * <15% false positive rate | ||
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1.1 | 156 | |
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2.1 | 157 | **Total Output Size:** Similar to POC1 (~220-350 words per analysis) |
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1.1 | 158 | |
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2.1 | 159 | == 2. Key Strategic Recommendations |
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1.1 | 160 | |
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2.1 | 161 | === Immediate Actions |
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1.1 | 162 | |
| 163 | **For POC:** | ||
| 164 | 1. Focus on core functionality only (claims + verdicts) | ||
| 165 | 2. Create basic explainer (1 page) | ||
| 166 | 3. Test AI quality without manual editing | ||
| 167 | 4. Make GO/NO-GO decision | ||
| 168 | |||
| 169 | **Planning:** | ||
| 170 | 1. Define accessibility strategy (when to build) | ||
| 171 | 2. Decide on multilingual priorities (which languages first) | ||
| 172 | 3. Research media verification options (partner vs build) | ||
| 173 | 4. Evaluate browser extension approach | ||
| 174 | |||
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2.1 | 175 | === Testing Strategy |
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1.1 | 176 | |
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2.1 | 177 | **POC Tests:** Can AI do this without humans? |
| 178 | **Beta Tests:** What do users need? What works? What doesn't? | ||
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1.1 | 179 | **Release Tests:** Is it production-ready? |
| 180 | |||
| 181 | **Key Principle:** Test assumptions before building features. | ||
| 182 | |||
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2.1 | 183 | === Build Sequence (Priority Order) |
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1.1 | 184 | |
| 185 | **Must Build:** | ||
| 186 | 1. Core analysis (claims + verdicts) ← POC | ||
| 187 | 2. Educational resources (basic → comprehensive) | ||
| 188 | 3. Accessibility (WCAG 2.1 AA) ← Legal requirement | ||
| 189 | |||
| 190 | **Should Build (Validate First):** | ||
| 191 | 4. Browser extensions ← Test demand | ||
| 192 | 5. Media verification ← Pilot with existing tools | ||
| 193 | 6. Multilingual ← Start with 2-3 languages | ||
| 194 | |||
| 195 | **Can Build Later:** | ||
| 196 | 7. Mobile apps ← PWA first | ||
| 197 | 8. ClaimReview schema ← After content library | ||
| 198 | 9. Export features ← Based on user requests | ||
| 199 | 10. Everything else ← Based on validation | ||
| 200 | |||
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2.1 | 201 | === Decision Framework |
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1.1 | 202 | |
| 203 | **For each feature, ask:** | ||
| 204 | 1. **Importance:** Risk + Impact + Strategy alignment? | ||
| 205 | 2. **Urgency:** Fail fast + Legal + Promises? | ||
| 206 | 3. **Validation:** Do we know users want this? | ||
| 207 | 4. **Priority:** When should we build it? | ||
| 208 | |||
| 209 | **Don't build anything without answering these questions.** | ||
| 210 | |||
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2.1 | 211 | == 4. Critical Principles |
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1.1 | 212 | |
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2.1 | 213 | === Automation First |
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1.1 | 214 | - AI makes content decisions |
| 215 | - Humans improve algorithms | ||
| 216 | - Scale through code, not people | ||
| 217 | |||
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2.1 | 218 | === Fail Fast |
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1.1 | 219 | - Test assumptions quickly |
| 220 | - Don't build unvalidated features | ||
| 221 | - Accept that experiments may fail | ||
| 222 | - Learn from failures | ||
| 223 | |||
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2.1 | 224 | === Evidence Over Authority |
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1.1 | 225 | - Transparent reasoning visible |
| 226 | - No single "true/false" verdicts | ||
| 227 | - Multiple scenarios shown | ||
| 228 | - Assumptions made explicit | ||
| 229 | |||
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2.1 | 230 | === User Focus |
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1.1 | 231 | - Serve users' needs first |
| 232 | - Build what's actually useful | ||
| 233 | - Don't build what's just "cool" | ||
| 234 | - Measure and iterate | ||
| 235 | |||
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2.1 | 236 | === Honest Assessment |
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1.1 | 237 | - Don't cherry-pick examples |
| 238 | - Document failures openly | ||
| 239 | - Accept limitations | ||
| 240 | - No overpromising | ||
| 241 | |||
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2.1 | 242 | == 5. POC Decision Gate |
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1.1 | 243 | |
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2.1 | 244 | === After POC, Choose: |
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1.1 | 245 | |
| 246 | **GO (Proceed to Beta):** | ||
| 247 | - AI quality ≥70% without editing | ||
| 248 | - Approach validated | ||
| 249 | - Team confident | ||
| 250 | - Clear path to improvement | ||
| 251 | |||
| 252 | **NO-GO (Pivot or Stop):** | ||
| 253 | - AI quality < 60% | ||
| 254 | - Requires manual editing for most | ||
| 255 | - Fundamental flaws identified | ||
| 256 | - Not feasible with current technology | ||
| 257 | |||
| 258 | **ITERATE (Improve & Retry):** | ||
| 259 | - Concept has merit | ||
| 260 | - Specific improvements identified | ||
| 261 | - Addressable with better prompts | ||
| 262 | - Test again after changes | ||
| 263 | |||
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2.1 | 264 | == 6. Key Risks & Mitigations |
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1.1 | 265 | |
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2.1 | 266 | === Risk 1: AI Quality Not Good Enough |
| 267 | **Mitigation:** Extensive prompt testing, use best models | ||
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1.1 | 268 | **Acceptance:** POC might fail - that's what testing reveals |
| 269 | |||
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2.1 | 270 | === Risk 2: Users Don't Understand Output |
| 271 | **Mitigation:** Create clear explainer, test with real users | ||
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1.1 | 272 | **Acceptance:** Iterate on explanation until comprehensible |
| 273 | |||
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2.1 | 274 | === Risk 3: Approach Doesn't Scale |
| 275 | **Mitigation:** Start simple, add complexity only when proven | ||
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1.1 | 276 | **Acceptance:** POC proves concept, beta proves scale |
| 277 | |||
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2.1 | 278 | === Risk 4: Legal/Compliance Issues |
| 279 | **Mitigation:** Plan accessibility early, consult legal experts | ||
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1.1 | 280 | **Acceptance:** Can't launch publicly without compliance |
| 281 | |||
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2.1 | 282 | === Risk 5: Feature Creep |
| 283 | **Mitigation:** Strict scope discipline, say NO to additions | ||
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1.1 | 284 | **Acceptance:** POC is minimal by design |
| 285 | |||
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2.1 | 286 | == 7. Success Metrics |
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1.1 | 287 | |
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2.1 | 288 | === POC Success |
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1.1 | 289 | - AI output quality ≥70% |
| 290 | - Manual editing needed < 30% of time | ||
| 291 | - Team confidence: High | ||
| 292 | - Decision: GO to beta | ||
| 293 | |||
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2.1 | 294 | === Platform Success (Later) |
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1.1 | 295 | - User comprehension ≥80% |
| 296 | - Return user rate ≥30% | ||
| 297 | - Flag rate (user corrections) < 10% | ||
| 298 | - Processing time < 30 seconds | ||
| 299 | - Error rate < 1% | ||
| 300 | |||
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2.1 | 301 | === Mission Success (Long-term) |
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1.1 | 302 | - Users make better-informed decisions |
| 303 | - Misinformation spread reduced | ||
| 304 | - Public discourse improves | ||
| 305 | - Trust in evidence increases | ||
| 306 | |||
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2.1 | 307 | == 8. What Makes FactHarbor Different |
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1.1 | 308 | |
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2.1 | 309 | === Not Traditional Fact-Checking |
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1.1 | 310 | - ❌ No simple "true/false" verdicts |
| 311 | - ✅ Multiple scenarios with context | ||
| 312 | - ✅ Transparent reasoning chains | ||
| 313 | - ✅ Explicit assumptions shown | ||
| 314 | |||
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2.1 | 315 | === Not AI Chatbot |
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1.1 | 316 | - ❌ Not conversational |
| 317 | - ✅ Structured Evidence Models | ||
| 318 | - ✅ Reproducible analysis | ||
| 319 | - ✅ Verifiable sources | ||
| 320 | |||
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2.1 | 321 | === Not Just Automation |
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1.1 | 322 | - ❌ Not replacing human judgment |
| 323 | - ✅ Augmenting human reasoning | ||
| 324 | - ✅ Making process transparent | ||
| 325 | - ✅ Enabling informed decisions | ||
| 326 | |||
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2.1 | 327 | == 9. Core Philosophy |
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1.1 | 328 | |
| 329 | **Three Pillars:** | ||
| 330 | |||
| 331 | **1. Scenarios Over Verdicts** | ||
| 332 | - Show multiple interpretations | ||
| 333 | - Make context explicit | ||
| 334 | - Acknowledge uncertainty | ||
| 335 | - Avoid false certainty | ||
| 336 | |||
| 337 | **2. Transparency Over Authority** | ||
| 338 | - Show reasoning, not just conclusions | ||
| 339 | - Make assumptions explicit | ||
| 340 | - Link to evidence | ||
| 341 | - Enable verification | ||
| 342 | |||
| 343 | **3. Evidence Over Opinions** | ||
| 344 | - Ground claims in sources | ||
| 345 | - Show supporting AND opposing evidence | ||
| 346 | - Evaluate source quality | ||
| 347 | - Avoid cherry-picking | ||
| 348 | |||
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2.1 | 349 | == 10. Next Actions |
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1.1 | 350 | |
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2.1 | 351 | === Immediate |
| 352 | □ Review this consolidated summary | ||
| 353 | □ Confirm POC scope agreement | ||
| 354 | □ Make strategic decisions on key questions | ||
| 355 | □ Begin POC development | ||
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1.1 | 356 | |
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2.1 | 357 | === Strategic Planning |
| 358 | □ Define accessibility approach | ||
| 359 | □ Select initial languages for multilingual | ||
| 360 | □ Research media verification partners | ||
| 361 | □ Evaluate browser extension frameworks | ||
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1.1 | 362 | |
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2.1 | 363 | === Continuous |
| 364 | □ Test assumptions before building | ||
| 365 | □ Measure everything | ||
| 366 | □ Learn from failures | ||
| 367 | □ Stay focused on mission | ||
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1.1 | 368 | |
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2.1 | 369 | == Summary of Summaries |
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1.1 | 370 | |
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2.1 | 371 | **POC Goal:** Prove AI can do this automatically |
| 372 | **POC Scope:** 4 simple components, ~200-300 words | ||
| 373 | **POC Critical:** Fully automated, no manual editing | ||
| 374 | **POC Success:** ≥70% quality without human correction | ||
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1.1 | 375 | |
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2.1 | 376 | **Gap Analysis:** 18 gaps identified, 2 critical (Accessibility + Education) |
| 377 | **Framework:** Importance (risk + impact + strategy) + Urgency (fail fast + legal + promises) | ||
| 378 | **Key Insight:** Context matters - urgency changes with milestones | ||
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1.1 | 379 | |
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2.1 | 380 | **Strategy:** Test first, build second. Fail fast. Stay focused. |
| 381 | **Philosophy:** Scenarios, transparency, evidence. No false certainty. | ||
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1.1 | 382 | |
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2.1 | 383 | == Document Status |
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1.1 | 384 | |
| 385 | **This document supersedes all previous analysis documents.** | ||
| 386 | |||
| 387 | All gap analysis, POC specifications, and strategic frameworks are consolidated here without timeline references. | ||
| 388 | |||
| 389 | **For detailed specifications, refer to:** | ||
| 390 | - User Needs document (in project knowledge) | ||
| 391 | - Requirements document (in project knowledge) | ||
| 392 | - This summary (comprehensive overview) | ||
| 393 | |||
| 394 | **Previous documents are archived for reference but this is the authoritative summary.** | ||
| 395 | |||
| 396 | **End of Consolidated Summary** | ||
| 397 |