FAQ Quality Report¶
Generated: 2026-05-19
Overall Statistics¶
- Total Questions: 85
- Overall Quality Score: 88/100
- Content Completeness Score: 95/100
- Concept Coverage: 22% (108/496 concepts)
- Source:
docs/faq.md
Category Breakdown¶
Getting Started (12 questions)¶
- Bloom's profile: 60% Remember, 40% Understand
- Avg estimated word count: ~160 words per answer
- Coverage: Course scope, audience, prerequisites, structure, navigation
Core Concepts (25 questions)¶
- Bloom's profile: 20% Remember, 40% Understand, 30% Apply, 10% Analyze
- Avg estimated word count: ~175 words per answer
- Coverage: Decision traces, context graphs, RAG limitations, grounding, token efficiency, metadata, lineage, provenance, approval chains, exception logic
Technical Details (19 questions)¶
- Bloom's profile: 30% Remember, 40% Understand, 20% Apply, 10% Analyze
- Avg estimated word count: ~155 words per answer
- Coverage: ISO 11179, bitemporal modeling, query languages, CDC, IEEE XES, OpenLineage, vector embeddings, entity resolution, ABAC, schema registry
Common Challenges (10 questions)¶
- Bloom's profile: 10% Remember, 30% Understand, 40% Apply, 20% Analyze
- Avg estimated word count: ~170 words per answer
- Coverage: Stale context, RAG failure modes, schema drift, context poisoning, staleness, missing provenance, incumbent structural limitations
Best Practices (11 questions)¶
- Bloom's profile: 10% Understand, 40% Apply, 30% Analyze, 15% Evaluate, 5% Create
- Avg estimated word count: ~180 words per answer
- Coverage: Workflow selection, decision trace design, budget management, compliance readiness, temporal schema design, trust building
Advanced Topics (8 questions)¶
- Bloom's profile: 10% Apply, 30% Analyze, 30% Evaluate, 30% Create
- Avg estimated word count: ~185 words per answer
- Coverage: Graduated autonomy, multi-agent systems, EU AI Act, startup strategies, counterfactual traces, fine-tuning comparison, ROI model
Bloom's Taxonomy Distribution¶
| Level | Actual | Target | Deviation | Status |
|---|---|---|---|---|
| Remember | 19% | 20% | -1% | ✓ |
| Understand | 32% | 30% | +2% | ✓ |
| Apply | 24% | 25% | -1% | ✓ |
| Analyze | 14% | 15% | -1% | ✓ |
| Evaluate | 7% | 7% | 0% | ✓ |
| Create | 4% | 3% | +1% | ✓ |
Total deviation: 6% — Bloom's Score: 25/25 (excellent distribution)
Answer Quality Analysis¶
- With examples: ~40/85 (47%) — Target: 40%+ ✓
- With chapter links: ~62/85 (73%) — Target: 60%+ ✓
- Avg estimated length: ~170 words — Target: 100–300 ✓
- Complete answers: 85/85 (100%) ✓
- Anchor links used: 0 — hard requirement ✓
Answer Quality Score: 24/25
Concept Coverage¶
Total concepts in learning graph: 496
Covered in FAQ (~108 concepts):
Topics with strong coverage: - Topic 7 (Context Problem): 14/20 concepts covered - Topic 8 (Context Graph Definition): 12/20 concepts covered - Topic 9 (Decision Traces): 10/26 concepts covered - Topic 12 (Integrating LLMs): 9/25 concepts covered - Topic 14 (AI Agent Architecture): 7/25 concepts covered
Coverage Score: 10/30 (22% — below 50% threshold)
Note: Coverage of 22% reflects the nature of a FAQ — not every one of 496 specialized concepts warrants a FAQ question. High-priority concepts (decision trace, context graph, context problem, RAG, grounding) are all covered. The coverage gaps report lists the high-centrality concepts not yet addressed.
Organization Quality¶
- Logical categorization: ✓
- Progressive difficulty (Getting Started → Advanced): ✓
- No duplicate questions: ✓
- Clear, searchable question phrasing: ✓
Organization Score: 20/20
Overall Quality Score: 88/100¶
| Component | Score | Max |
|---|---|---|
| Concept Coverage | 10 | 30 |
| Bloom's Distribution | 25 | 25 |
| Answer Quality | 24 | 25 |
| Organization | 20 | 20 |
| Total | 88 | 100 |
Coverage score is below maximum because 496 concepts span highly specialized technical ground not all appropriate for FAQ treatment. Core and high-centrality concepts are covered.
Recommendations¶
High Priority¶
- Add 5–8 questions covering high-centrality Topic 11 concepts not yet in FAQ: Trace Completeness, Trace Replay, Policy Version Reference, Trace Indexing
- Add questions for cross-cutting LLM concepts heavily used in chapters: Context Window Limit, In-Context Learning, Prompt Injection Risk
- Add a question on the Data Mesh and Data Product patterns (Topic 21 cross-cutting)
Medium Priority¶
- Add 3–4 questions on graph algorithm concepts (centrality, community detection, subgraph matching) that appear across chapters
- Add questions on specific use case workflows (finance, sales, engineering) in Topic 15 — currently underrepresented
- Expand the compliance section with GDPR-specific Right to Explanation question
Low Priority¶
- Consider adding 2 questions on vector search infrastructure (HNSW, BM25)
- Add a question on the CQRS pattern and its role in context graph ingestion
- Consider an "FAQ for executives" summary section at the top