Changes for page POC Summary (POC1 & POC2)

Last modified by Robert Schaub on 2026/02/08 08:23

From 1.1 to 2.1 From 2.2 to 2.3

From version 2.1

edited by Robert Schaub
on 2025/12/24 21:53

Change comment: Imported from XAR

To version 2.2

edited by Robert Schaub
on 2026/01/20 20:29

Change comment: Renamed back-links.

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@@ -4,7 +4,7 @@
  {{info}}
  **This page describes POC1 v0.4+ (3-stage pipeline with caching).**
--For complete implementation details, see [[POC1 API & Schemas Specification>>FactHarbor.Specification.POC.API-and-Schemas.WebHome]].
++For complete implementation details, see [[POC1 API & Schemas Specification>>Archive.FactHarbor.Specification.POC.API-and-Schemas.WebHome]].
  {{/info}}
@@ -12,15 +12,17 @@
  == 1. POC Specification ==
  === POC Goal
--Prove that AI can extract claims and determine verdicts automatically without human intervention.
++Prove that AI can extract claims and determine verdicts automatically without human intervention. ===
--=== POC Output (4 Components Only)
++=== POC Output (4 Components Only) ===
++*
++**
  **1. ANALYSIS SUMMARY**
  - 3-5 sentences
  - How many claims found
  - Distribution of verdicts
--- Overall assessment
++- Overall assessment**
  **2. CLAIMS IDENTIFICATION**
  - 3-5 numbered factual claims
@@ -34,9 +34,9 @@
  - 3-5 sentences
  - Neutral summary of article content
--**Total output: ~200-300 words**
++**Total output: 200-300 words**
--=== What's NOT in POC
++=== What's NOT in POC ===
  ❌ Scenarios (multiple interpretations)
  ❌ Evidence display (supporting/opposing lists)
@@ -48,13 +48,13 @@
  ❌ Export, sharing features
  ❌ Any other features
--=== Critical Requirement
++=== Critical Requirement ===
  **FULLY AUTOMATED - NO MANUAL EDITING**
  This is non-negotiable. POC tests whether AI can do this without human intervention.
--=== POC Success Criteria
++=== POC Success Criteria ===
  **Passes if:**
  - ✅ AI extracts 3-5 factual claims automatically
@@ -69,7 +69,7 @@
  - ❌ Requires manual editing for most analyses (> 50%)
  - ❌ Team loses confidence in approach
--=== POC Architecture
++=== POC Architecture ===
  **Frontend:** Simple input form + results display
  **Backend:** Single API call to Claude (Sonnet 4.5)
@@ -76,7 +76,7 @@
  **Processing:** One prompt generates complete analysis
  **Database:** None required (stateless)
--=== POC Philosophy
++=== POC Philosophy ===
  > "Build less, learn more, decide faster. Test the hardest part first."
@@ -87,6 +87,7 @@
  **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.
  **Solution (POC1 Test):** Approach 1 - Single-Pass Holistic Analysis
++
  * Enhanced AI prompt to evaluate logical structure
  * AI identifies main argument and assesses if it follows from evidence
  * Article verdict may differ from claim average
@@ -93,6 +93,7 @@
  * Zero additional cost, no architecture changes
  **Testing:**
++
  * 30-article test set
  * Success: ≥70% accuracy detecting misleading articles
  * Marked as experimental
@@ -102,11 +102,14 @@
  == 2. POC2 Specification ==
  === POC2 Goal ===
++
  Prove that AKEL produces high-quality outputs consistently at scale with complete quality validation.
  === POC2 Enhancements (From POC1) ===
--**1. COMPLETE QUALITY GATES (All 4)**
++*
++**
++**1. COMPLETE QUALITY GATES (All 4)
  * Gate 1: Claim Validation (from POC1)
  * Gate 2: Evidence Relevance ← NEW
  * Gate 3: Scenario Coherence ← NEW
@@ -113,6 +113,7 @@
  * Gate 4: Verdict Confidence (from POC1)
  **2. EVIDENCE DEDUPLICATION (FR54)**
++
  * Prevent counting same source multiple times
  * Handle syndicated content (AP, Reuters)
  * Content fingerprinting with fuzzy matching
@@ -119,6 +119,7 @@
  * Target: >95% duplicate detection accuracy
  **3. CONTEXT-AWARE ANALYSIS (Conditional)**
++
  * **If POC1 succeeds (≥70%):** Implement as standard feature
  * **If POC1 promising (50-70%):** Try weighted aggregation approach
  * **If POC1 fails (<50%):** Defer to post-POC2
@@ -125,6 +125,7 @@
  * Detects articles with accurate claims but misleading conclusions
  **4. QUALITY METRICS DASHBOARD (NFR13)**
++
  * Track hallucination rates
  * Monitor gate performance
  * Evidence quality metrics
@@ -141,26 +141,30 @@
  === Success Criteria ===
  **Quality:**
++
  * Hallucination rate <5% (target: <3%)
  * Average quality rating ≥8.0/10
  * Gates identify >95% of low-quality outputs
  **Performance:**
++
  * All 4 quality gates operational
  * Evidence deduplication >95% accurate
  * Quality metrics tracked continuously
  **Context-Aware (if implemented):**
++
  * Maintains ≥70% accuracy detecting misleading articles
  * <15% false positive rate
--**Total Output Size:** Similar to POC1 (~220-350 words per analysis)
++**Total Output Size:** Similar to POC1 (220-350 words per analysis)
--== 2. Key Strategic Recommendations
++== 2. Key Strategic Recommendations ==
--=== Immediate Actions
++=== Immediate Actions ===
  **For POC:**
++
 . Focus on core functionality only (claims + verdicts)
 . Create basic explainer (1 page)
 . Test AI quality without manual editing
@@ -167,12 +167,13 @@
 . Make GO/NO-GO decision
  **Planning:**
++
 . Define accessibility strategy (when to build)
 . Decide on multilingual priorities (which languages first)
 . Research media verification options (partner vs build)
 . Evaluate browser extension approach
--=== Testing Strategy
++=== Testing Strategy ===
  **POC Tests:** Can AI do this without humans?
  **Beta Tests:** What do users need? What works? What doesn't?
@@ -180,9 +180,10 @@
  **Key Principle:** Test assumptions before building features.
--=== Build Sequence (Priority Order)
++=== Build Sequence (Priority Order) ===
  **Must Build:**
++
 . Core analysis (claims + verdicts) ← POC
 . Educational resources (basic → comprehensive)
 . Accessibility (WCAG 2.1 AA) ← Legal requirement
@@ -198,9 +198,10 @@
 . Export features ← Based on user requests
 . Everything else ← Based on validation
--=== Decision Framework
++=== Decision Framework ===
  **For each feature, ask:**
++
 . **Importance:** Risk + Impact + Strategy alignment?
 . **Urgency:** Fail fast + Legal + Promises?
 . **Validation:** Do we know users want this?
@@ -208,40 +208,40 @@
  **Don't build anything without answering these questions.**
--== 4. Critical Principles
++== 4. Critical Principles ==
  === Automation First
  - AI makes content decisions
  - Humans improve algorithms
--- Scale through code, not people
++- Scale through code, not people ===
  === Fail Fast
  - Test assumptions quickly
  - Don't build unvalidated features
  - Accept that experiments may fail
--- Learn from failures
++- Learn from failures ===
  === Evidence Over Authority
  - Transparent reasoning visible
  - No single "true/false" verdicts
  - Multiple scenarios shown
--- Assumptions made explicit
++- Assumptions made explicit ===
  === User Focus
  - Serve users' needs first
  - Build what's actually useful
  - Don't build what's just "cool"
--- Measure and iterate
++- Measure and iterate ===
  === Honest Assessment
  - Don't cherry-pick examples
  - Document failures openly
  - Accept limitations
--- No overpromising
++- No overpromising ===
--== 5. POC Decision Gate
++== 5. POC Decision Gate ==
--=== After POC, Choose:
++=== After POC, Choose: ===
  **GO (Proceed to Beta):**
  - AI quality ≥70% without editing
@@ -261,35 +261,35 @@
  - Addressable with better prompts
  - Test again after changes
--== 6. Key Risks & Mitigations
++== 6. Key Risks & Mitigations ==
  === Risk 1: AI Quality Not Good Enough
  **Mitigation:** Extensive prompt testing, use best models
--**Acceptance:** POC might fail - that's what testing reveals
++**Acceptance:** POC might fail - that's what testing reveals ===
  === Risk 2: Users Don't Understand Output
  **Mitigation:** Create clear explainer, test with real users
--**Acceptance:** Iterate on explanation until comprehensible
++**Acceptance:** Iterate on explanation until comprehensible ===
  === Risk 3: Approach Doesn't Scale
  **Mitigation:** Start simple, add complexity only when proven
--**Acceptance:** POC proves concept, beta proves scale
++**Acceptance:** POC proves concept, beta proves scale ===
  === Risk 4: Legal/Compliance Issues
  **Mitigation:** Plan accessibility early, consult legal experts
--**Acceptance:** Can't launch publicly without compliance
++**Acceptance:** Can't launch publicly without compliance ===
  === Risk 5: Feature Creep
  **Mitigation:** Strict scope discipline, say NO to additions
--**Acceptance:** POC is minimal by design
++**Acceptance:** POC is minimal by design ===
--== 7. Success Metrics
++== 7. Success Metrics ==
  === POC Success
  - AI output quality ≥70%
  - Manual editing needed < 30% of time
  - Team confidence: High
--- Decision: GO to beta
++- Decision: GO to beta ===
  === Platform Success (Later)
  - User comprehension ≥80%
@@ -296,43 +296,45 @@
  - Return user rate ≥30%
  - Flag rate (user corrections) < 10%
  - Processing time < 30 seconds
--- Error rate < 1%
++- Error rate < 1% ===
  === Mission Success (Long-term)
  - Users make better-informed decisions
  - Misinformation spread reduced
  - Public discourse improves
--- Trust in evidence increases
++- Trust in evidence increases ===
--== 8. What Makes FactHarbor Different
++== 8. What Makes FactHarbor Different ==
  === Not Traditional Fact-Checking
  - ❌ No simple "true/false" verdicts
  - ✅ Multiple scenarios with context
  - ✅ Transparent reasoning chains
--- ✅ Explicit assumptions shown
++- ✅ Explicit assumptions shown ===
  === Not AI Chatbot
  - ❌ Not conversational
  - ✅ Structured Evidence Models
  - ✅ Reproducible analysis
--- ✅ Verifiable sources
++- ✅ Verifiable sources ===
  === Not Just Automation
  - ❌ Not replacing human judgment
  - ✅ Augmenting human reasoning
  - ✅ Making process transparent
--- ✅ Enabling informed decisions
++- ✅ Enabling informed decisions ===
--== 9. Core Philosophy
++== 9. Core Philosophy ==
  **Three Pillars:**
++*
++**
  **1. Scenarios Over Verdicts**
  - Show multiple interpretations
  - Make context explicit
  - Acknowledge uncertainty
--- Avoid false certainty
++- Avoid false certainty**
  **2. Transparency Over Authority**
  - Show reasoning, not just conclusions
@@ -346,30 +346,30 @@
  - Evaluate source quality
  - Avoid cherry-picking
--== 10. Next Actions
++== 10. Next Actions ==
  === Immediate
  □ Review this consolidated summary
  □ Confirm POC scope agreement
  □ Make strategic decisions on key questions
--□ Begin POC development
++□ Begin POC development  ===
  === Strategic Planning
  □ Define accessibility approach
  □ Select initial languages for multilingual
  □ Research media verification partners
--□ Evaluate browser extension frameworks
++□ Evaluate browser extension frameworks  ===
  === Continuous
  □ Test assumptions before building
  □ Measure everything
  □ Learn from failures
--□ Stay focused on mission
++□ Stay focused on mission  ===
--== Summary of Summaries
++== Summary of Summaries ==
  **POC Goal:** Prove AI can do this automatically
--**POC Scope:** 4 simple components, ~200-300 words
++**POC Scope:** 4 simple components, 200-300 words
  **POC Critical:** Fully automated, no manual editing
  **POC Success:** ≥70% quality without human correction
@@ -380,7 +380,7 @@
  **Strategy:** Test first, build second. Fail fast. Stay focused.
  **Philosophy:** Scenarios, transparency, evidence. No false certainty.
--== Document Status
++== Document Status ==
  **This document supersedes all previous analysis documents.**
@@ -394,4 +394,3 @@
  **Previous documents are archived for reference but this is the authoritative summary.**
  **End of Consolidated Summary**
--

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