Changes for page POC Requirements (POC1 & POC2)

Last modified by Robert Schaub on 2026/02/08 08:26

From 2.2 to 2.1

From version 2.1

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

Change comment: Imported from XAR

To version 1.1

edited by Robert Schaub
on 2025/12/19 16:13

Change comment: Imported from XAR

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@@ -1,1 +1,1 @@
--POC Requirements (POC1 & POC2)
++POC Requirements

Content

@@ -1,18 +1,11 @@
  = POC Requirements =
--
--{{info}}
--**POC1 Architecture:** 3-stage AKEL pipeline (Extract → Analyze → Holistic) with Redis caching, credit tracking, and LLM abstraction layer.
--
--See [[POC1 API Specification>>FactHarbor.Specification.POC.API-and-Schemas.WebHome]] for complete technical details.
--{{/info}}
--
--
--
--**Status:** ✅ Approved for Development
--**Version:** 2.0 (Updated after Specification Cross-Check)
++**Status:** ✅ Approved for Development
++**Version:** 2.0 (Updated after Specification Cross-Check)
  **Goal:** Prove that AI can extract claims and determine verdicts automatically without human intervention
++---
++
  == 1. POC Overview ==
  === 1.1 What POC Tests ===
@@ -33,6 +33,8 @@
  * Perfect accuracy
  * Complete feature set
++---
++
  === 1.2 Scenarios Deferred to POC2 ===
  **Intentional Simplification:**
@@ -66,65 +66,33 @@
  Claims → Verdicts (scenarios implicit in reasoning)
  {{/code}}
++---
++
  == 2. POC Output Specification ==
--=== 2.1 Component 1: ANALYSIS SUMMARY (Context-Aware) ===
++=== 2.1 Component 1: ANALYSIS SUMMARY ===
--**What:** Context-aware overview that considers both individual claims AND their relationship to the article's main argument
++**What:** Brief overview of findings
++**Length:** 3-5 sentences
++**Content:**
++* How many claims found
++* Distribution of verdicts
++* Overall assessment
--**Length:** 4-6 sentences
--
--**Content (Required Elements):**
--1. **Article's main thesis/claim** - What is the article trying to argue or prove?
--2. **Claim count and verdicts** - How many claims analyzed, distribution of verdicts
--3. **Central vs. supporting claims** - Which claims are central to the article's argument?
--4. **Relationship assessment** - Do the claims support the article's conclusion?
--5. **Overall credibility** - Final assessment considering claim importance
--
--**Critical Innovation:**
--
--POC1 tests whether AI can understand that **article credibility ≠ simple average of claim verdicts**. An article might:
--* Make accurate supporting facts but draw unsupported conclusions
--* Have one false central claim that invalidates the whole argument
--* Misframe accurate information to mislead
--
--**Good Example (Context-Aware):**
++**Example:**
  {{code}}
--This article argues that coffee cures cancer based on its antioxidant
--content. We analyzed 3 factual claims: 2 about coffee's chemical
--properties are well-supported, but the main causal claim is refuted
--by current evidence. The article confuses correlation with causation.
--Overall assessment: MISLEADING - makes an unsupported medical claim
--despite citing some accurate facts.
++This article makes 4 claims about coffee's health effects. We found
++2 claims are well-supported, 1 is uncertain, and 1 is refuted.
++Overall assessment: mostly accurate with some exaggeration.
  {{/code}}
--**Poor Example (Simple Aggregation - Don't Do This):**
--{{code}}
--This article makes 3 claims. 2 are well-supported and 1 is refuted.
--Overall assessment: mostly accurate (67% accurate).
--{{/code}}
--↑ This misses that the refuted claim IS the article's main point!
++---
--**What POC1 Tests:**
--
--Can AI identify and assess:
--* ✅ The article's main thesis/conclusion?
--* ✅ Which claims are central vs. supporting?
--* ✅ Whether the evidence supports the conclusion?
--* ✅ Overall credibility considering logical structure?
--
--**If AI Cannot Do This:**
--
--That's valuable to learn in POC1! We'll:
--* Note as limitation
--* Fall back to simple aggregation with warning
--* Design explicit article-level analysis for POC2
--
  === 2.2 Component 2: CLAIMS IDENTIFICATION ===
--**What:** List of factual claims extracted from article
--**Format:** Numbered list
--**Quantity:** 3-5 claims
++**What:** List of factual claims extracted from article
++**Format:** Numbered list
++**Quantity:** 3-5 claims
  **Requirements:**
  * Factual claims only (not opinions/questions)
  * Clearly stated
@@ -140,10 +140,12 @@
  [4] Coffee prevents Alzheimer's completely
  {{/code}}
++---
++
  === 2.3 Component 3: CLAIMS VERDICTS ===
--**What:** Verdict for each claim identified
--**Format:** Per claim structure
++**What:** Verdict for each claim identified
++**Format:** Per claim structure
  **Required Elements:**
  * **Verdict Label:** WELL-SUPPORTED / PARTIALLY SUPPORTED / UNCERTAIN / REFUTED
@@ -170,15 +170,17 @@
  **Risk Tier Display:**
  * **Tier A (Red):** High Risk - Medical/Legal/Safety/Elections
--* **Tier B (Yellow):** Medium Risk - Policy/Science/Causality
++* **Tier B (Yellow):** Medium Risk - Policy/Science/Causality
  * **Tier C (Green):** Low Risk - Facts/Definitions/History
  **Note:** Risk tier shown for demonstration purposes in POC. Full system uses risk tiers to determine review workflow.
++---
++
  === 2.4 Component 4: ARTICLE SUMMARY (Optional) ===
--**What:** Brief summary of original article content
--**Length:** 3-5 sentences
++**What:** Brief summary of original article content
++**Length:** 3-5 sentences
  **Tone:** Neutral (article's position, not FactHarbor's analysis)
  **Example:**
@@ -190,60 +190,17 @@
  to disease prevention. Recommends 2-3 cups daily for optimal health.
  {{/code}}
--=== 2.5 Component 5: USAGE STATISTICS (Cost Tracking) ===
++---
--**What:** LLM usage metrics for cost optimization and scaling decisions
++=== 2.5 Total Output Size ===
--**Purpose:**
--* Understand cost per analysis
--* Identify optimization opportunities
--* Project costs at scale
--* Inform architecture decisions
--
--**Display Format:**
--{{code}}
--USAGE STATISTICS:
--• Article: 2,450 words (12,300 characters)
--• Input tokens: 15,234
--• Output tokens: 892
--• Total tokens: 16,126
--• Estimated cost: $0.24 USD
--• Response time: 8.3 seconds
--• Cost per claim: $0.048
--• Model: claude-sonnet-4-20250514
--{{/code}}
--
--**Why This Matters:**
--
--At scale, LLM costs are critical:
--* 10,000 articles/month ≈ $200-500/month
--* 100,000 articles/month ≈ $2,000-5,000/month
--* Cost optimization can reduce expenses 30-50%
--
--**What POC1 Learns:**
--* How cost scales with article length
--* Prompt optimization opportunities (caching, compression)
--* Output verbosity tradeoffs
--* Model selection strategy (FAST vs. REASONING roles)
--* Article length limits (if needed)
--
--**Implementation:**
--* Claude API already returns usage data
--* No extra API calls needed
--* Display to user + log for aggregate analysis
--* Test with articles of varying lengths
--
--**Critical for GO/NO-GO:** Unit economics must be viable at scale!
--
--=== 2.6 Total Output Size ===
--
--**Combined:** ~220-350 words
--* Analysis Summary (Context-Aware): 60-90 words (4-6 sentences)
++**Combined:** ~200-300 words
++* Analysis Summary: 50-70 words
  * Claims Identification: 30-50 words
  * Claims Verdicts: 100-150 words
  * Article Summary: 30-50 words (optional)
--**Note:** Analysis summary is slightly longer (4-6 sentences vs. 3-5) to accommodate context-aware assessment of article structure and logical reasoning.
++---
  == 3. What's NOT in POC Scope ==
@@ -291,6 +291,8 @@
  * ❌ Analytics
  * ❌ A/B testing
++---
++
  == 4. POC Simplifications vs. Full System ==
  === 4.1 Architecture Comparison ===
@@ -298,7 +298,7 @@
  **POC Architecture (Simplified):**
  {{code}}
  User Input → Single AKEL Call → Output Display
-- (all processing)
++           (all processing)
  {{/code}}
  **Full System Architecture:**
@@ -319,6 +319,8 @@
  |Data Model|Stateless (no database)|PostgreSQL + Redis + S3
  |Architecture|Single prompt to Claude|AKEL Orchestrator + Components
++---
++
  === 4.2 Workflow Comparison ===
  **POC1 Workflow:**
@@ -336,6 +336,8 @@
 . **Time Evolution** (versioning, re-evaluation triggers)
  **Total: 6 phases with quality gates, ~10-30 seconds**
++---
++
  === 4.3 Why POC is Simplified ===
  **Engineering Rationale:**
@@ -354,6 +354,8 @@
  * ❌ POC doesn't validate scale (test in Beta)
  * ❌ POC doesn't validate scenario architecture (design in POC2)
++---
++
  === 4.4 Gap Between POC1 and POC2/Beta ===
  **What needs to be built for POC2:**
@@ -373,6 +373,8 @@
  **POC1 → POC2 is significant architectural expansion.**
++---
++
  == 5. Publication Mode & Labeling ==
  === 5.1 POC Publication Mode ===
@@ -386,20 +386,22 @@
  * All quality gates active (simplified)
  * Risk tier classification shown (demo)
++---
++
  === 5.2 User-Facing Labels ===
  **Primary Label (top of analysis):**
  {{code}}
  ╔════════════════════════════════════════════════════════════╗
--║ [AI-GENERATED - POC/DEMO] ║
--║ ║
--║ This analysis was produced entirely by AI and has not ║
--║ been human-reviewed. Use for demonstration purposes. ║
--║ ║
--║ Source: AI/AKEL v1.0 (POC) ║
--║ Review Status: Not Reviewed (Proof-of-Concept) ║
--║ Quality Gates: 4/4 Passed (Simplified) ║
--║ Last Updated: [timestamp] ║
++║  [AI-GENERATED - POC/DEMO]                                ║
++║                                                            ║
++║  This analysis was produced entirely by AI and has not    ║
++║  been human-reviewed. Use for demonstration purposes.     ║
++║                                                            ║
++║  Source: AI/AKEL v1.0 (POC)                               ║
++║  Review Status: Not Reviewed (Proof-of-Concept)          ║
++║  Quality Gates: 4/4 Passed (Simplified)                  ║
++║  Last Updated: [timestamp]                                ║
  ╚════════════════════════════════════════════════════════════╝
  {{/code}}
@@ -408,6 +408,8 @@
  * **[Risk: B]** 🟡 Medium Risk (Policy/Science)
  * **[Risk: C]** 🟢 Low Risk (Facts/Definitions)
++---
++
  === 5.3 Display Requirements ===
  **Must Show:**
@@ -425,6 +425,8 @@
  * Authoritative verdicts
  * Complete accuracy
++---
++
  === 5.4 Mode 2 vs. Full System Publication ===
  |=Element|=POC Mode 2|=Full System Mode 2|=Full System Mode 3
@@ -435,6 +435,8 @@
  |Risk Display|Demo only|Workflow-integrated|Validated
  |User Actions|View only|Flag for review|Trust rating
++---
++
  == 6. Quality Gates (Simplified Implementation) ==
  === 6.1 Overview ===
@@ -453,6 +453,8 @@
  * Failures displayed to user (not blocking)
  * Full system has comprehensive validation
++---
++
  === 6.2 Gate 1: Source Quality (Basic) ===
  **Full System Requirements:**
@@ -473,6 +473,8 @@
  **Failure Handling:** Display error message, don't generate verdict
++---
++
  === 6.3 Gate 2: Contradiction Search (Basic) ===
  **Full System Requirements:**
@@ -495,6 +495,8 @@
  **Failure Handling:** Note "limited contradiction search" in output
++---
++
  === 6.4 Gate 3: Uncertainty Quantification (Basic) ===
  **Full System Requirements:**
@@ -515,6 +515,8 @@
  **Failure Handling:** Show "Confidence: Unknown" if calculation fails
++---
++
  === 6.5 Gate 4: Structural Integrity (Basic) ===
  **Full System Requirements:**
@@ -535,6 +535,8 @@
  **Failure Handling:** Display error message
++---
++
  === 6.6 Quality Gate Display ===
  **POC shows simplified status:**
@@ -557,6 +557,8 @@
  Note: This analysis has limited evidence. Use with caution.
  {{/code}}
++---
++
  === 6.7 Simplified vs. Full System ===
  |=Gate|=POC (Simplified)|=Full System
@@ -567,12 +567,14 @@
  **POC Goal:** Demonstrate that quality gates are possible, not perfect implementation.
++---
++
  == 7. AKEL Architecture Comparison ==
  === 7.1 POC AKEL (Simplified) ===
  **Implementation:**
--* Single provider API call (REASONING model)
++* Single Claude API call (Sonnet 4.5)
  * One comprehensive prompt
  * All processing in single request
  * No separate components
@@ -584,10 +584,10 @@
 . Extract 3-5 factual claims
 . For each claim:
-- - Determine verdict (WELL-SUPPORTED/PARTIALLY/UNCERTAIN/REFUTED)
-- - Assign confidence score (0-100%)
-- - Assign risk tier (A/B/C)
-- - Write brief reasoning (1-3 sentences)
++   - Determine verdict (WELL-SUPPORTED/PARTIALLY/UNCERTAIN/REFUTED)
++   - Assign confidence score (0-100%)
++   - Assign risk tier (A/B/C)
++   - Write brief reasoning (1-3 sentences)
 . Generate analysis summary (3-5 sentences)
 . Generate article summary (3-5 sentences)
 . Run basic quality checks
@@ -597,6 +597,8 @@
  **Processing Time:** 10-18 seconds (estimate)
++---
++
  === 7.2 Full System AKEL (Production) ===
  **Architecture:**
@@ -621,6 +621,8 @@
  **Processing Time:** 10-30 seconds (full pipeline)
++---
++
  === 7.3 Why POC Uses Single Call ===
  **Advantages:**
@@ -643,6 +643,8 @@
  Full component architecture comes in Beta after POC validates concept.
++---
++
  === 7.4 Evolution Path ===
  **POC1:** Single prompt → Prove concept
@@ -650,6 +650,8 @@
  **Beta:** Multi-component AKEL → Production architecture
  **Release 1.0:** Full AKEL + Federation → Scale
++---
++
  == 8. Functional Requirements ==
  === FR-POC-1: Article Input ===
@@ -673,6 +673,8 @@
  * User can paste URL of article
  * System accepts input and triggers analysis
++---
++
  === FR-POC-2: Claim Extraction (Fully Automated) ===
  **Requirement:** AI automatically extracts 3-5 factual claims
@@ -700,6 +700,8 @@
  * Claims are clearly stated
  * No manual editing required
++---
++
  === FR-POC-3: Verdict Generation (Fully Automated) ===
  **Requirement:** AI automatically generates verdict for each claim
@@ -706,11 +706,11 @@
  **Functionality:**
  * For each claim, AI:
-- * Evaluates claim based on available evidence/knowledge
-- * Determines verdict: WELL-SUPPORTED / PARTIALLY SUPPORTED / UNCERTAIN / REFUTED
-- * Assigns confidence score (0-100%)
-- * Assigns risk tier (A/B/C)
-- * Writes brief reasoning (1-3 sentences)
++  * Evaluates claim based on available evidence/knowledge
++  * Determines verdict: WELL-SUPPORTED / PARTIALLY SUPPORTED / UNCERTAIN / REFUTED
++  * Assigns confidence score (0-100%)
++  * Assigns risk tier (A/B/C)
++  * Writes brief reasoning (1-3 sentences)
  * System displays verdict for each claim
  **Critical:** NO MANUAL EDITING ALLOWED
@@ -732,6 +732,8 @@
  * Verdict is defensible given reasoning
  * All generated automatically by AI
++---
++
  === FR-POC-4: Analysis Summary (Fully Automated) ===
  **Requirement:** AI generates brief summary of analysis
@@ -738,9 +738,9 @@
  **Functionality:**
  * AI summarizes findings in 3-5 sentences:
-- * How many claims found
-- * Distribution of verdicts
-- * Overall assessment
++  * How many claims found
++  * Distribution of verdicts
++  * Overall assessment
  * System displays at top of results
  **Critical:** NO MANUAL EDITING ALLOWED
@@ -751,6 +751,8 @@
  * 3-5 sentences
  * Automatically generated
++---
++
  === FR-POC-5: Article Summary (Fully Automated, Optional) ===
  **Requirement:** AI generates brief summary of original article
@@ -770,6 +770,8 @@
  * 3-5 sentences
  * Automatically generated
++---
++
  === FR-POC-6: Publication Mode Display ===
  **Requirement:** Clear labeling of AI-generated content
@@ -787,6 +787,8 @@
  * Risk tiers are color-coded
  * Quality gate status is visible
++---
++
  === FR-POC-7: Quality Gate Execution ===
  **Requirement:** Execute simplified quality gates
@@ -804,6 +804,8 @@
  * Failures explained to user
  * Gates don't block publication (POC mode)
++---
++
  == 9. Non-Functional Requirements ==
  === NFR-POC-1: Fully Automated Processing ===
@@ -822,8 +822,8 @@
  **Pipeline:**
  {{code}}
  User Input → AKEL Processing → Output Display
-- ↓
-- ZERO human editing
++           ↓
++     ZERO human editing
  {{/code}}
  **If AI output is poor:**
@@ -837,6 +837,8 @@
  * Validates scalability (humans can't review every analysis)
  * Honest test of technical feasibility
++---
++
  === NFR-POC-2: Performance ===
  **Requirement:** Analysis completes in reasonable time
@@ -856,6 +856,8 @@
  * User sees loading indicator
  * No timeout errors
++---
++
  === NFR-POC-3: Reliability ===
  **Requirement:** System works for manual testing sessions
@@ -875,6 +875,8 @@
  * Errors are handled gracefully
  * User receives clear error messages
++---
++
  === NFR-POC-4: Environment ===
  **Requirement:** Runs on simple infrastructure
@@ -892,48 +892,8 @@
  * Auto-scaling
  * Disaster recovery
--=== NFR-POC-5: Cost Efficiency Tracking ===
++---
--**Requirement:** Track and display LLM usage metrics to inform optimization decisions
--
--**Must Track:**
--* Input tokens (article + prompt)
--* Output tokens (generated analysis)
--* Total tokens
--* Estimated cost (USD)
--* Response time (seconds)
--* Article length (words/characters)
--
--**Must Display:**
--* Usage statistics in UI (Component 5)
--* Cost per analysis
--* Cost per claim extracted
--
--**Must Log:**
--* Aggregate metrics for analysis
--* Cost distribution by article length
--* Token efficiency trends
--
--**Purpose:**
--* Understand unit economics
--* Identify optimization opportunities
--* Project costs at scale
--* Inform architecture decisions (caching, model selection, etc.)
--
--**Acceptance Criteria:**
--* ✅ Usage data displayed after each analysis
--* ✅ Metrics logged for aggregate analysis
--* ✅ Cost calculated accurately (Claude API pricing)
--* ✅ Test cases include varying article lengths
--* ✅ POC1 report includes cost analysis section
--
--**Success Target:**
--* Average cost per analysis < $0.05 USD
--* Cost scaling behavior understood (linear/exponential)
--* 2+ optimization opportunities identified
--
--**Critical:** Unit economics must be viable for scaling decision!
--
  == 10. Technical Architecture ==
  === 10.1 System Components ===
@@ -945,7 +945,7 @@
  **Backend:**
  * Single API endpoint
--* Calls provider API (REASONING model; configured via LLM abstraction)
++* Calls Claude API (Sonnet 4.5 or latest)
  * Parses response
  * Returns JSON to frontend
@@ -957,32 +957,36 @@
  * Claude API (Anthropic) - required
  * Optional: URL fetch service for article text extraction
++---
++
  === 10.2 Processing Flow ===
  {{code}}
 . User submits text or URL
-- ↓
++   ↓
 . Backend receives request
-- ↓
++   ↓
 . If URL: Fetch article text
-- ↓
++   ↓
 . Call Claude API with single prompt:
-- "Extract claims, evaluate each, provide verdicts"
-- ↓
++   "Extract claims, evaluate each, provide verdicts"
++   ↓
 . Claude API returns:
-- - Analysis summary
-- - Claims list
-- - Verdicts for each claim (with risk tiers)
-- - Article summary (optional)
-- - Quality gate results
-- ↓
++   - Analysis summary
++   - Claims list
++   - Verdicts for each claim (with risk tiers)
++   - Article summary (optional)
++   - Quality gate results
++   ↓
 . Backend parses response
-- ↓
++   ↓
 . Frontend displays results with Mode 2 labeling
  {{/code}}
  **Key Simplification:** Single API call does entire analysis
++---
++
  === 10.3 AI Prompt Strategy ===
  **Single Comprehensive Prompt:**
@@ -989,49 +989,27 @@
  {{code}}
  Task: Analyze this article and provide:
--1. Identify the article's main thesis/conclusion
-- - What is the article trying to argue or prove?
-- - What is the primary claim or conclusion?
++1. Extract 3-5 factual claims from the article
++2. For each claim:
++   - Determine verdict (WELL-SUPPORTED / PARTIALLY SUPPORTED / UNCERTAIN / REFUTED)
++   - Assign confidence score (0-100%)
++   - Assign risk tier (A: Medical/Legal/Safety, B: Policy/Science, C: Facts/Definitions)
++   - Write brief reasoning (1-3 sentences)
++3. Run quality gates:
++   - Check: ≥2 sources found
++   - Attempt: Basic contradiction search
++   - Calculate: Confidence scores
++   - Verify: Structural integrity
++4. Write analysis summary (3-5 sentences: claims found, verdict distribution, overall assessment)
++5. Write article summary (3-5 sentences: neutral summary of article content)
--2. Extract 3-5 factual claims from the article
-- - Note which claims are CENTRAL to the main thesis
-- - Note which claims are SUPPORTING facts
--
--3. For each claim:
-- - Determine verdict (WELL-SUPPORTED / PARTIALLY SUPPORTED / UNCERTAIN / REFUTED)
-- - Assign confidence score (0-100%)
-- - Assign risk tier (A: Medical/Legal/Safety, B: Policy/Science, C: Facts/Definitions)
-- - Write brief reasoning (1-3 sentences)
--
--4. Assess relationship between claims and main thesis:
-- - Do the claims actually support the article's conclusion?
-- - Are there logical leaps or unsupported inferences?
-- - Is the article's framing misleading even if individual facts are accurate?
--
--5. Run quality gates:
-- - Check: ≥2 sources found
-- - Attempt: Basic contradiction search
-- - Calculate: Confidence scores
-- - Verify: Structural integrity
--
--6. Write context-aware analysis summary (4-6 sentences):
-- - State article's main thesis
-- - Report claims found and verdict distribution
-- - Note if central claims are problematic
-- - Assess whether evidence supports conclusion
-- - Overall credibility considering claim importance
--
--7. Write article summary (3-5 sentences: neutral summary of article content)
--
  Return as structured JSON with quality gate results.
  {{/code}}
  **One prompt generates everything.**
--**Critical Addition:**
++---
--Steps 1, 2 (marking central claims), 4, and 6 are NEW for context-aware analysis. These test whether AI can distinguish between "accurate facts poorly reasoned" vs. "genuinely credible article."
--
  === 10.4 Technology Stack Suggestions ===
  **Frontend:**
@@ -1046,7 +1046,7 @@
  **AKEL Integration:**
  * Claude API via Anthropic SDK
--* Model: Provider-default REASONING model or latest available
++* Model: Claude Sonnet 4.5 or latest available
  **Database:**
  * None (stateless acceptable)
@@ -1057,6 +1057,8 @@
  * Local development environment sufficient for POC
  * Optional: Deploy to cloud for remote demos
++---
++
  == 11. Success Criteria ==
  === 11.1 Minimum Success (POC Passes) ===
@@ -1070,9 +1070,6 @@
  * ✅ Team/advisors understand the output
  * ✅ Team agrees approach has merit
  * ✅ **Minimal or no manual editing needed** (< 30% of analyses require manual intervention)
--* ✅ **Cost efficiency acceptable** (average cost per analysis < $0.05 USD target)
--* ✅ **Cost scaling understood** (data collected on article length vs. cost)
--* ✅ **Optimization opportunities identified** (≥2 potential improvements documented)
  **Quality Definition:**
  * "Reasonable verdict" = Defensible given general knowledge
@@ -1079,6 +1079,8 @@
  * "Coherent summary" = Logically structured, grammatically correct
  * "Comprehensible" = Reviewers understand what analysis means
++---
++
  === 11.2 POC Fails If ===
  **Automatic NO-GO if any of these:**
@@ -1088,6 +1088,8 @@
  * ❌ **Requires manual editing for most analyses** (> 50% need human correction)
  * ❌ Team loses confidence in AI-automated approach
++---
++
  === 11.3 Quality Thresholds ===
  **POC quality expectations:**
@@ -1113,6 +1113,8 @@
  * Understandable reasoning
  * Useful output
++---
++
  == 12. Test Cases ==
  === 12.1 Test Case 1: Simple Factual Claim ===
@@ -1128,6 +1128,8 @@
  **Success:** Verdict is reasonable and reasoning makes sense
++---
++
  === 12.2 Test Case 2: Complex News Article ===
  **Input:** News article URL with multiple claims about politics/health/science
@@ -1141,6 +1141,8 @@
  **Success:** Claims identified are actually from article, verdicts are reasonable
++---
++
  === 12.3 Test Case 3: Controversial Topic ===
  **Input:** Article on contested political or scientific topic
@@ -1153,6 +1153,8 @@
  **Success:** Analysis is fair and doesn't show obvious bias
++---
++
  === 12.4 Test Case 4: Clearly False Claim ===
  **Input:** Article with obviously false claim (e.g., "The Earth is flat")
@@ -1166,6 +1166,8 @@
  **Success:** AI correctly identifies false claim with high confidence
++---
++
  === 12.5 Test Case 5: Genuinely Uncertain Claim ===
  **Input:** Article with claim where evidence is genuinely mixed
@@ -1178,6 +1178,8 @@
  **Success:** AI recognizes uncertainty and doesn't overstate confidence
++---
++
  === 12.6 Test Case 6: High-Risk Medical Claim ===
  **Input:** Article making medical claims
@@ -1191,6 +1191,8 @@
  **Success:** Risk tier correctly assigned, appropriate warnings shown
++---
++
  == 13. POC Decision Gate ==
  === 13.1 Decision Framework ===
@@ -1213,6 +1213,8 @@
  * Expand to Evidence Model structure
  * Test with more complex articles
++---
++
  **Option B: NO-GO (Pivot or Stop)**
  **Conditions:**
@@ -1226,6 +1226,8 @@
  * **Pivot:** Change to hybrid human-AI approach (accept manual review required)
  * **Stop:** Conclude approach not viable, revisit later
++---
++
  **Option C: ITERATE (Improve POC)**
  **Conditions:**
@@ -1240,33 +1240,39 @@
  * Re-run POC with improvements
  * Then make GO/NO-GO decision
++---
++
  === 13.2 Decision Criteria Summary ===
  {{code}}
--AI Quality < 60% → NO-GO (approach doesn't work)
++AI Quality < 60%  → NO-GO (approach doesn't work)
  AI Quality 60-70% → ITERATE (improve and retry)
--AI Quality ≥70% → GO (proceed to POC2)
++AI Quality ≥70%   → GO (proceed to POC2)
  {{/code}}
++---
++
  == 14. Key Risks & Mitigations ==
  === 14.1 Risk: AI Quality Not Good Enough ===
--**Likelihood:** Medium-High
--**Impact:** POC fails
++**Likelihood:** Medium-High
++**Impact:** POC fails
  **Mitigation:**
  * Extensive prompt engineering and testing
--* Use best available AI models (role-based selection; configured via LLM abstraction)
++* Use best available AI models (Sonnet 4.5)
  * Test with diverse article types
  * Iterate on prompts based on results
  **Acceptance:** This is what POC tests - be ready for failure
++---
++
  === 14.2 Risk: AI Consistency Issues ===
--**Likelihood:** Medium
--**Impact:** Works sometimes, fails other times
++**Likelihood:** Medium
++**Impact:** Works sometimes, fails other times
  **Mitigation:**
  * Test with 10+ diverse articles
@@ -1275,10 +1275,12 @@
  **Acceptance:** Some variability OK if average quality ≥70%
++---
++
  === 14.3 Risk: Output Incomprehensible ===
--**Likelihood:** Low-Medium
--**Impact:** Users can't understand analysis
++**Likelihood:** Low-Medium
++**Impact:** Users can't understand analysis
  **Mitigation:**
  * Create clear explainer document
@@ -1288,10 +1288,12 @@
  **Acceptance:** Iterate until comprehensible
++---
++
  === 14.4 Risk: API Rate Limits / Costs ===
--**Likelihood:** Low
--**Impact:** System slow or expensive
++**Likelihood:** Low
++**Impact:** System slow or expensive
  **Mitigation:**
  * Monitor API usage
@@ -1300,10 +1300,12 @@
  **Acceptance:** POC can be slow and expensive (optimization later)
++---
++
  === 14.5 Risk: Scope Creep ===
--**Likelihood:** Medium
--**Impact:** POC becomes too complex
++**Likelihood:** Medium
++**Impact:** POC becomes too complex
  **Mitigation:**
  * Strict scope discipline
@@ -1312,6 +1312,8 @@
  **Acceptance:** POC is minimal by design
++---
++
  == 15. POC Philosophy ==
  === 15.1 Core Principles ===
@@ -1343,21 +1343,27 @@
  * Document failures openly
  * Make data-driven decisions
++---
++
  === 15.2 What POC Is ===
--✅ Testing AI capability without humans
--✅ Proving core technical concept
--✅ Fast validation of approach
--✅ Honest assessment of feasibility
++✅ Testing AI capability without humans
++✅ Proving core technical concept
++✅ Fast validation of approach
++✅ Honest assessment of feasibility
++---
++
  === 15.3 What POC Is NOT ===
--❌ Building a product
--❌ Production-ready system
--❌ Feature-complete platform
--❌ Perfectly accurate analysis
--❌ Polished user experience
++❌ Building a product
++❌ Production-ready system
++❌ Feature-complete platform
++❌ Perfectly accurate analysis
++❌ Polished user experience
++---
++
  == 16. Success = Clear Path Forward ==
  **If POC succeeds (≥70% AI quality):**
@@ -1375,63 +1375,18 @@
  **Either way, POC provides clarity.**
++---
++
  == 17. Related Pages ==
--* [[User Needs>>FactHarbor.Specification.Requirements.User Needs.WebHome]]
--* [[Requirements>>FactHarbor.Specification.Requirements.WebHome]]
--* [[Gap Analysis>>FactHarbor.Specification.Requirements.GapAnalysis]]
++* [[User Needs>>FactHarbor.Specification.Requirements.User Needs]]
++* [[Requirements>>FactHarbor.Requirements.WebHome]]
++* [[Gap Analysis>>FactHarbor.Analysis.GapAnalysis]]
  * [[Architecture>>FactHarbor.Specification.Architecture.WebHome]]
  * [[AKEL>>FactHarbor.Specification.AI Knowledge Extraction Layer (AKEL).WebHome]]
  * [[Workflows>>FactHarbor.Specification.Workflows.WebHome]]
++---
++
  **Document Status:** ✅ Ready for POC Development (Version 2.0 - Updated with Spec Alignment)
--
--=== NFR-POC-11: LLM Provider Abstraction (POC1) ===
--
--**Requirement:** POC1 MUST implement LLM abstraction layer with support for multiple providers.
--
--**POC1 Implementation:**
--
--* **Primary Provider:** Anthropic Claude API
-- * Stage 1: Provider-default FAST model
-- * Stage 2: Provider-default REASONING model (cached)
-- * Stage 3: Provider-default REASONING model
--
--* **Provider Interface:** Abstract LLMProvider interface implemented
--
--* **Configuration:** Environment variables for provider selection
-- * {{code}}LLM_PRIMARY_PROVIDER=anthropic{{/code}}
-- * {{code}}LLM_STAGE1_MODEL=claude-haiku-4{{/code}}
-- * {{code}}LLM_STAGE2_MODEL=claude-sonnet-3-5{{/code}}
--
--* **Failover:** Basic error handling with cache fallback for Stage 2
--
--* **Cost Tracking:** Log provider name and cost per request
--
--**Future (POC2/Beta):**
--
--* Secondary provider (OpenAI) with automatic failover
--* Admin API for runtime provider switching
--* Cost comparison dashboard
--* Cross-provider output verification
--
--**Success Criteria:**
--
--* All LLM calls go through abstraction layer (no direct API calls)
--* Provider can be changed via environment variable without code changes
--* Cost tracking includes provider name in logs
--* Stage 2 falls back to cache on provider failure
--
--**Implementation:** See [[POC1 API & Schemas Specification>>Test.FactHarbor.Specification.POC.API-and-Schemas.WebHome]] Section 6
--
--**Dependencies:**
--* NFR-14 (Main Requirements)
--* Design Decision 9
--* Architecture Section 2.2
--
--**Priority:** HIGH (P1)
--
--**Rationale:** Even though POC1 uses single provider, abstraction must be in place from start to avoid costly refactoring later.
--
--

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