KNOWLEDGE / Evaluation / LLMs
Designing Useful AI Evaluations
A layered approach to evaluating retrieval, generation, tool use, cost, and latency.
EvaluationLLMsQuality
- DOMAIN
- AI Engineering
- LEVEL
- Advanced
- READ
- 10 min
- UPDATED
- Aug 25, 2026
MENTAL MODEL / KEY IDEAS
Keep these in mind
- 01Start from user failure modes
- 02Mix deterministic and judged metrics
- 03Inspect slices, not only averages
What to evaluate
An end-to-end score hides which stage failed. Useful evaluation separates retrieval, reasoning, tool execution, response quality, and operational constraints.
- Stage metrics
- Product outcomes
- Cost and latency budgets
Build the dataset
A compact set of representative and adversarial cases is more useful than a large synthetic dataset with no connection to product behavior.
- Real examples
- Known hard cases
- Clear expected behavior
Read the results
Treat evaluation as debugging evidence. Segment results by intent, source, and difficulty before changing the system.