FAQ Quality Report

Generated: 2026-09-18

Overall Statistics

Total Questions: 97
Overall Quality Score: 70/100
Content Completeness Score (inputs available before generation): 100/100
Concept Coverage (strict phrase-match against the 528-concept Learning Graph): 39.0% (206/528)

Content Completeness Assessment

Before generating the FAQ, the four required inputs were checked:

Input	Finding	Score
Course description	7 audience-specific course descriptions in Course Descriptions, each with title, audience, prerequisites, duration, and Bloom's-aligned learning outcomes	25/25
Learning graph	learning-graph.csv — 528 concepts, valid DAG (0 cycles, per prior validation run)	25/25
Glossary	glossary.md — 528 terms, one per Learning Graph concept	15/15
Chapter content	27 chapters, 98,836 words	20/20
Concept-to-chapter coverage	100% — every one of the 528 Learning Graph concepts appears in some chapter's "Concepts Covered" table	15/15
Total		100/100

Content completeness was excellent, so the FAQ was generated without a reduced-content disclaimer.

Category Breakdown

Category	Questions	Avg Words	Examples	Linked	Dominant Bloom's Levels
Getting Started Questions	12	119	8 (67%)	12 (100%)	Remember (7), Understand (3), Apply (2)
Core Concepts	28	120	15 (54%)	28 (100%)	Understand (15), Remember (6), Analyze (4), Apply (3)
Technical Detail Questions	22	114	11 (50%)	22 (100%)	Understand (11), Remember (7), Apply (2), Analyze (2)
Common Challenge Questions	15	124	7 (47%)	15 (100%)	Analyze (7), Understand (6), Apply (2)
Best Practice Questions	11	118	2 (18%)	11 (100%)	Apply (6), Evaluate (4), Analyze (1)
Advanced Topic Questions	9	129	1 (11%)	9 (100%)	Analyze (6), Create (2), Evaluate (1)
Total	97	120	44 (45%)	97 (100%)

Bloom's Taxonomy Distribution

Actual vs. target (target is the book-wide blend implied by the per-category targets in the skill definition, weighted by this FAQ's actual category sizes):

Level	Actual	Target	Deviation
Remember	20.6%	20%	+0.6% ✓
Understand	36.1%	30%	+6.1%
Apply	15.5%	25%	−9.5% ✗
Analyze	20.6%	15%	+5.6%
Evaluate	5.2%	7%	−1.8% ✓
Create	2.1%	3%	−0.9% ✓

Total absolute deviation: 24.5 percentage points → 15/25 points (21–30% deviation tier).

Interpretation: the FAQ under-represents Apply-level questions and over-represents Understand. This is a direct consequence of how the Learning Graph's vocabulary skews for this book: a large share of the 528 concepts are precise technical definitions (graph theory, data architecture, AI terminology) that naturally produce "What is X?" / "What is the difference between X and Y?" questions (Understand) rather than "How do I do X?" questions (Apply). The Common Challenge and Best Practice categories were written Apply/Analyze-heavy to compensate, and that shows in their per-category breakdown above, but it wasn't enough to fully offset the Understand-heavy Core Concepts and Technical Detail categories.

Answer Quality Analysis

Examples: 44/97 (45%) — Target: 40%+ → 7/7 ✓
Links: 97/97 (100%) — Target: 60%+ → 7/7 ✓
Avg Length: 120 words — within the 100–300 word target range for every single answer (0 answers under 100 words, 0 over 300) → 6/6 ✓
Complete Answers: 97/97 (100%) — every answer directly and fully addresses its question → 5/5 ✓

Answer Quality Score: 25/25

Concept Coverage

Strict phrase-match coverage: 206/528 concepts (39.0%) — a concept counts as "covered" only if its exact Learning Graph label (or the label with a trailing " Archetype" / parenthetical stripped) appears somewhere in faq.md's text.

This headline number understates real coverage for two structural reasons, both worth knowing before treating 39.0% as the final word:

The Learning Graph is unusually fine-grained (528 concepts). By comparison, the skill's own reference example reaches 73% coverage — but that implies a substantially smaller concept universe. A curated, non-redundant FAQ of any reasonable size will always under-cover a graph this granular.
Coverage is not evenly distributed — it's concentrated on the concepts that matter most. Breaking coverage down by the Learning Graph's own taxonomy shows the FAQ is strongest exactly where centrality is highest:

Taxonomy Category	Covered	Total	%
System Dynamics & Feedback (DYNM)	65	97	67%
Systems Thinking Foundations (FOUND)	29	43	67%
Systems Archetypes (ARCH)	17	36	47%
Graph Theory & Graph Databases (GRPH)	15	32	47%
Knowledge Representation & Data (KREP)	16	39	41%
Artificial Intelligence Systems (ARTI)	12	38	32%
Leverage Points & Emergence (LEVR)	13	42	31%
Enterprise Knowledge Graphs (ENTK)	15	48	31%
Knowledge Systems & Economic Complexity (KSEC)	5	20	25%
Systems Thinking Across Disciplines (DISC)	3	13	23%
Knowledge Graph Applications (KGAP)	4	25	16%
Archetype Case Studies & Named Examples (CASE)	9	69	13%
Systems Design & Future Practice (DSGN)	3	26	12%

The lowest-coverage category, CASE (13%), is 69 concepts that are individually-named worked examples inside archetype chapters (e.g., "Bacteria Growth," "Moore's Law," "Steam Engines" as instances of Limits to Growth) rather than teachable vocabulary — the FAQ intentionally covers the archetype each example illustrates rather than writing a separate question per instance. Excluding CASE concepts, coverage across the remaining 459 "core vocabulary" concepts is 197/459 = 42.9%.

Cross-checking against chapter-level concept-impact scores (a rough centrality measure derived from each chapter's concept table) confirms the FAQ prioritized high-centrality concepts as instructed: of the 40 highest-impact concepts in the entire book, all but a small handful are directly covered by a dedicated FAQ question.

Coverage Score: 10/30 (< 50% tier — see Coverage Gaps Report for the prioritized list of what's still missing)

Organization Quality

Logical categorization: ✓ (6 categories, Getting Started → Advanced Topics)
Progressive difficulty: ✓ (easy → hard roughly tracks category order; verified via per-question Bloom's/difficulty tagging)
No duplicates: ✓ (0 duplicate questions found by automated check)
Clear, searchable questions: ✓ (all questions end in "?", use Learning Graph terminology, 5–15 words)
Zero anchor links: ✓ (0 of 61 unique internal links use a # fragment — hard requirement met)
Zero broken links: ✓ (all 61 unique internal link targets verified to exist on disk)

Organization Score: 20/20

Overall Quality Score: 70/100

Coverage: 10/30
Bloom's Distribution: 15/25
Answer Quality: 25/25
Organization: 20/20

The score is held down almost entirely by the Coverage component, for the structural reasons explained above — Answer Quality and Organization are both at or near maximum. This is a curated, high-precision FAQ (every answer well within word-count target, 100% linked, 0 broken/anchor links, 0 duplicates) that trades broad concept-list coverage for depth and accuracy on the concepts readers are most likely to actually ask about.

Recommendations

High Priority

Add questions for the highest-impact concepts still uncovered (see Coverage Gaps Report for the full prioritized list):

Loop Marker and Balancing Loop Label (CLD notation details, Chapter 3)
Linear Relationship (Chapter 6, pairs with the linear-vs-exponential-growth question already present)
Network Topology (Chapter 12/19)
Model Assumptions and Model Limitations (Chapter 2 — validating a systems map)
Data Quality and Normalized Data Model (Chapter 18)

Medium Priority

Add more Apply-level questions to Core Concepts and Technical Detail Questions to correct the Bloom's-distribution deficit (target: 6–8 additional "How do I use X" / "When should I apply X" questions).
Expand coverage in the three weakest taxonomy categories — Knowledge Graph Applications (KGAP, 16%), Systems Design & Future Practice (DSGN, 12%), Knowledge Systems & Economic Complexity (KSEC, 20%) — each would benefit from 3–5 targeted questions in a future revision.

Low Priority

Consider 2–3 more Evaluate-level Advanced Topics questions (currently only 1 of 9) to round out that category's Bloom's spread.
Re-run the coverage check after any future chapter content additions, since new chapters will add new Learning Graph concepts that the FAQ won't yet reference.