Chapter 06
Building Evals That Matter
Define what the eval is measuring, why it matters to users, and how the oracle works. Compare public benchmarks to product-specific evals; do not inherit benchmark blind spots uncritically. For ranking/search, use position-aware metrics like NDCG only when the user experience…
Apply this chapter
- Define what the eval is measuring, why it matters to users, and how the oracle works.
- Compare public benchmarks to product-specific evals; do not inherit benchmark blind spots uncritically.
- For ranking/search, use position-aware metrics like NDCG only when the user experience really depends on order.
- Build weighted quality metrics that reflect product risk, not leaderboard theater.
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Save your product context once, then open a focused conversation that combines it with this concept.
11 focused briefs
Concepts in this chapter
- 036Evals and BenchmarksBenchmarks are useful signals, but many evals are narrower, noisier, or less well-defined than their leaderboard numbers suggest.
- 037Real-World EvalsPublic evals are useful because they make measurement concrete. They are also limited because every eval measures a particular shape of task.
- 038Adversarial and Red-Team SamplingRandom samples estimate normal behavior. Adversarial samples reveal what happens when users push the system.
- 039Eval Data ManagementIf prompts, datasets, rubrics, labels, judges, and model versions are not versioned, the evaluation cannot be trusted.
- 040NDCG for Search RelevanceNDCG helps Confidence Engineers measure whether the most relevant search and ranking results appear where users will actually see them.
- 041Stop Chasing High-Water MarksIf you rerun a noisy evaluation enough times, variance will eventually hand you a beautiful score. That does not make the system better.
- 042AI Passing Testing Certification ExamsWhen AI can pass certification-style testing exams, the human advantage moves from memorizing terminology to designing evidence.
- 043Building a Quality MetricEvery AI team needs at least one quality metric that turns messy behavior into release evidence.
- 044The Asymptotic Curve of AI QualityAI quality usually improves quickly at first, then gets harder, slower, and never reaches perfection.
- 045Benchmarking: Quality Is Relative NowWhen absolute truth is hard to measure, relative quality can still tell you whether you are competitive, broken, unusual, or missing something…
- 187Aesthetic Judgment of AI OutputAI output can be correct and still feel cheap, awkward, off-brand, or untrustworthy.