POC Summary (POC1 & POC2)

1. POC Specification

POC Goal
Prove that AI can extract claims and determine verdicts automatically without human intervention.

POC Output (4 Components Only)

• • 1. ANALYSIS SUMMARY
       - 3-5 sentences
       - How many claims found
       - Distribution of verdicts  
       - Overall assessment

2. CLAIMS IDENTIFICATION
- 3-5 numbered factual claims
- Extracted automatically by AI

3. CLAIMS VERDICTS
- Per claim: Verdict label + Confidence % + Brief reasoning (1-3 sentences)
- Verdict labels: WELL-SUPPORTED / PARTIALLY SUPPORTED / UNCERTAIN / REFUTED

4. ARTICLE SUMMARY (optional)
- 3-5 sentences
- Neutral summary of article content

Total output: 200-300 words

What's NOT in POC

❌ Scenarios (multiple interpretations)  
❌ Evidence display (supporting/opposing lists)  
❌ Source links  
❌ Detailed reasoning chains  
❌ User accounts, history, search  
❌ Browser extensions, API  
❌ Accessibility, multilingual, mobile  
❌ Export, sharing features  
❌ Any other features

Critical Requirement

FULLY AUTOMATED - NO MANUAL EDITING

This is non-negotiable. POC tests whether AI can do this without human intervention.

POC Success Criteria

Passes if:
- ✅ AI extracts 3-5 factual claims automatically
- ✅ AI provides reasonable verdicts (≥70% make sense)
- ✅ Output is comprehensible
- ✅ Team agrees approach has merit
- ✅ Minimal or no manual editing needed

Fails if:
- ❌ Claim extraction poor (< 60% accuracy)
- ❌ Verdicts nonsensical (< 60% reasonable)
- ❌ Requires manual editing for most analyses (> 50%)
- ❌ Team loses confidence in approach

POC Architecture

Frontend: Simple input form + results display  
Backend: Single API call to Claude (Sonnet 4.5)  
Processing: One prompt generates complete analysis  
Database: None required (stateless)

POC Philosophy

 "Build less, learn more, decide faster. Test the hardest part first."

Context-Aware Analysis (Experimental POC1 Feature)

Problem: Article credibility ≠ simple average of claim verdicts

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
• Zero additional cost, no architecture changes

Testing:

• 30-article test set
• Success: ≥70% accuracy detecting misleading articles
• Marked as experimental

See: Article Verdict Problem for full analysis and solution approaches.

2. Key Strategic Recommendations

Immediate Actions

For POC:

1. Focus on core functionality only (claims + verdicts)
   2. Create basic explainer (1 page)
   3. Test AI quality without manual editing
   4. Make GO/NO-GO decision

Planning:

1. Define accessibility strategy (when to build)
   2. Decide on multilingual priorities (which languages first)
   3. Research media verification options (partner vs build)
   4. Evaluate browser extension approach

Testing Strategy

POC Tests: Can AI do this without humans?  
Beta Tests: What do users need? What works? What doesn't?  
Release Tests: Is it production-ready?

Key Principle: Test assumptions before building features.

Build Sequence (Priority Order)

Must Build:

1. Core analysis (claims + verdicts) ← POC
   2. Educational resources (basic → comprehensive)
   3. Accessibility (WCAG 2.1 AA) ← Legal requirement

Should Build (Validate First):
4. Browser extensions ← Test demand
5. Media verification ← Pilot with existing tools
6. Multilingual ← Start with 2-3 languages

Can Build Later:
7. Mobile apps ← PWA first
8. ClaimReview schema ← After content library
9. Export features ← Based on user requests
10. Everything else ← Based on validation

Decision Framework

For each feature, ask:

1. Importance: Risk + Impact + Strategy alignment?
   2. Urgency: Fail fast + Legal + Promises?
   3. Validation: Do we know users want this?
   4. Priority: When should we build it?

Don't build anything without answering these questions.

4. Critical Principles

Automation First
- AI makes content decisions
- Humans improve algorithms
- Scale through code, not people

Fail Fast
- Test assumptions quickly
- Don't build unvalidated features
- Accept that experiments may fail
- Learn from failures

Evidence Over Authority
- Transparent reasoning visible
- No single "true/false" verdicts
- Multiple scenarios shown
- Assumptions made explicit

User Focus
- Serve users' needs first
- Build what's actually useful
- Don't build what's just "cool"
- Measure and iterate

Honest Assessment
- Don't cherry-pick examples
- Document failures openly
- Accept limitations
- No overpromising

5. POC Decision Gate

After POC, Choose:

GO (Proceed to Beta):
- AI quality ≥70% without editing
- Approach validated
- Team confident
- Clear path to improvement

NO-GO (Pivot or Stop):
- AI quality < 60%
- Requires manual editing for most
- Fundamental flaws identified
- Not feasible with current technology

ITERATE (Improve & Retry):
- Concept has merit
- Specific improvements identified
- Addressable with better prompts
- Test again after changes

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

Risk 2: Users Don't Understand Output
Mitigation: Create clear explainer, test with real users  
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

Risk 4: Legal/Compliance Issues
Mitigation: Plan accessibility early, consult legal experts  
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

7. Success Metrics

POC Success
- AI output quality ≥70%
- Manual editing needed < 30% of time
- Team confidence: High
- Decision: GO to beta

Platform Success (Later)
- User comprehension ≥80%
- Return user rate ≥30%
- Flag rate (user corrections) < 10%
- Processing time < 30 seconds
- Error rate < 1%

Mission Success (Long-term)
- Users make better-informed decisions
- Misinformation spread reduced
- Public discourse improves
- Trust in evidence increases

8. What Makes FactHarbor Different

Not Traditional Fact-Checking
- ❌ No simple "true/false" verdicts
- ✅ Multiple scenarios with context
- ✅ Transparent reasoning chains
- ✅ Explicit assumptions shown

Not AI Chatbot
- ❌ Not conversational
- ✅ Structured Evidence Models
- ✅ Reproducible analysis
- ✅ Verifiable sources

Not Just Automation
- ❌ Not replacing human judgment
- ✅ Augmenting human reasoning
- ✅ Making process transparent
- ✅ Enabling informed decisions

9. Core Philosophy

Three Pillars:

• • 1. Scenarios Over Verdicts
       - Show multiple interpretations
       - Make context explicit
       - Acknowledge uncertainty
       - Avoid false certainty

2. Transparency Over Authority
- Show reasoning, not just conclusions
- Make assumptions explicit
- Link to evidence
- Enable verification

3. Evidence Over Opinions
- Ground claims in sources
- Show supporting AND opposing evidence
- Evaluate source quality
- Avoid cherry-picking

10. Next Actions

Immediate
□ Review this consolidated summary  
□ Confirm POC scope agreement  
□ Make strategic decisions on key questions  
□ Begin POC development  

Strategic Planning
□ Define accessibility approach  
□ Select initial languages for multilingual  
□ Research media verification partners  
□ Evaluate browser extension frameworks  

Continuous
□ Test assumptions before building  
□ Measure everything  
□ Learn from failures  
□ Stay focused on mission  

Summary of Summaries

POC Goal: Prove AI can do this automatically  
POC Scope: 4 simple components, 200-300 words  
POC Critical: Fully automated, no manual editing  
POC Success: ≥70% quality without human correction  

Gap Analysis: 18 gaps identified, 2 critical (Accessibility + Education)  
Framework: Importance (risk + impact + strategy) + Urgency (fail fast + legal + promises)  
Key Insight: Context matters - urgency changes with milestones  

Strategy: Test first, build second. Fail fast. Stay focused.  
Philosophy: Scenarios, transparency, evidence. No false certainty.  

Document Status

This document supersedes all previous analysis documents.

All gap analysis, POC specifications, and strategic frameworks are consolidated here without timeline references.

For detailed specifications, refer to:
- User Needs document (in project knowledge)
- Requirements document (in project knowledge)
- This summary (comprehensive overview)

Previous documents are archived for reference but this is the authoritative summary.

End of Consolidated Summary