Course Description Assessment: Token Optimization
Overall Score: 100/100
Quality Rating: Excellent — Ready for learning graph generation
Detailed Scoring Breakdown
| Element | Max | Earned | Notes |
|---|---|---|---|
| Title | 5 | 5 | "Token Optimization: Measuring, Analyzing, and Reducing the Cost of Generative AI" — descriptive and specific |
| Target Audience | 5 | 5 | Professional development; primary and secondary audiences both named |
| Prerequisites | 5 | 5 | Five concrete prerequisites listed plus optional helpful background |
| Main Topics Covered | 10 | 10 | 20 topics spanning theory, vendor APIs, observability, A/B testing, and harness tools |
| Topics Excluded | 5 | 5 | Eight explicit out-of-scope items (training, GPU optimization, multimodal, etc.) |
| Learning Outcomes Header | 5 | 5 | "After completing this course, students will be able to:" present |
| Remember | 10 | 10 | 7 specific recall outcomes covering vocabulary, pricing, APIs, and harnesses |
| Understand | 10 | 10 | 8 outcomes explaining mechanisms (BPE, caching, batch, agentic loops) |
| Apply | 10 | 10 | 8 hands-on procedures (instrumenting logs, configuring caching, batch jobs, harness budgets) |
| Analyze | 10 | 10 | 7 decomposition outcomes (log analysis, cost attribution, cache miss diagnosis) |
| Evaluate | 10 | 10 | 7 judgment outcomes (statistical significance, vendor lock-in, privacy/compliance) |
| Create | 10 | 10 | 6 design outcomes plus 3 capstone project options |
| Descriptive Context | 5 | 5 | Three-paragraph overview establishes business importance and vendor pluralism |
Gap Analysis
No gaps identified. Every scoring element was awarded full points.
Minor observations (not gaps, but worth tracking as the learning graph develops):
- The course covers three vendor ecosystems (Anthropic, OpenAI, Google). The learning graph should ensure roughly balanced concept coverage across all three rather than weighting one ecosystem.
- Prompt caching is a high-leverage topic that appears across multiple Bloom levels. The learning graph should treat it as a hub concept with several connected child concepts (cache key design, hit rate measurement, invariants).
- Agentic harness cost control (Claude Code, Codex, Antigravity) is a relatively new area with thinner public reference material; concept generation may need to lean on first-principles reasoning rather than canonical terminology.
Improvement Suggestions
The course description does not require revision before learning graph generation. As the learning graph is built, consider these enrichments:
- If the 200-concept target is hard to reach, expand the tokenization topic into vendor-specific subtopics (e.g., Claude's tokenizer vs. OpenAI's tiktoken vs. Gemini's SentencePiece-based tokenizer) — each comparison generates several concepts.
- The A/B testing methodology topic can be deepened with concepts from causal inference (CUPED, sequential testing, multi-armed bandits) if more concepts are needed.
- The observability topic can be expanded with concrete tooling concepts (OpenTelemetry semantic conventions for LLMs, log aggregation systems, cost dashboards) if breadth is needed.
Concept Generation Readiness
Estimated reachable concepts: 220–280
The course description has the breadth and depth needed to generate well over 200 concepts:
- 20 main topics × ~10 concepts per topic ≈ 200 concepts from topics alone
- 45+ specific Bloom's outcomes, each suggesting 1–3 additional fine-grained concepts
- Three vendor ecosystems multiply concept count for cross-cutting topics (APIs, pricing, harnesses, caching)
- Capstone project options introduce additional applied concepts (dashboards, budget policies, routing layers)
The description is ready for the learning-graph-generator skill.
Next Steps
The course description scores 100/100 and is ready to proceed with learning graph generation.
Recommended next action: Run the learning-graph-generator skill to produce the 200-concept learning graph for Token Optimization.