Deep systems understanding · Study map 78 of 85
Quality gates: fast wrong answers are wasted work
Suggested preparation: Load testing: recreate the traffic, not just the average
Picture it
Think of swapping the engine in a race car to make it faster. Even if the speedometer shows improvement, you still need an expert driver to verify it can take corners without spinning out. A performance win is an illusion if the new system quietly starts hallucinating facts, breaking formats, or failing edge cases that matter to real users.
What to understand
- Golden sets, task slices, retrieval/agent evaluation, and regressions
- LLM-judge bias, human calibration, contamination, and release gates
Follow the references
1 · Code + guide
EleutherAI · Language Model Evaluation Harness ↗Read task configuration and reproducibility guidance; retain examples, not just totals.
2 · Book chapter
Google SRE Workbook · Canarying releases ↗Adapt canary decision criteria to both model quality and service performance.
Make it concrete
Create a quality gate covering difficult prompts, long contexts, tool schemas, refusals where appropriate, and grounded answers. Compare against the unchanged baseline.
You understand it when…
Which tests catch a quantization change that preserves general benchmarks but breaks structured extraction?
This is a study map. Follow the reference for the full explanation; use the question to test your mental model. Browse the reference shelf.