Evaluation
Evaluation measures whether an AI system behaves correctly on realistic examples.
Add evaluation before you optimize, scale, or trust an AI feature with important user outcomes.
Use when
- RAG answer quality
- Agent reliability
- Regression testing prompts
- Comparing model or retrieval changes
Avoid when
- Vague goals with no success criteria
- One-off demos
- Purely subjective taste without examples
- Teams unwilling to inspect failures
Why evaluation comes early
Evaluation is how you stop guessing. It gives you a repeatable way to compare prompts, models, retrieval settings, tools, and guardrails.
For MVPs, evaluation can be lightweight: a spreadsheet of realistic examples, expected properties, and failure notes. The important part is that changes can be compared against the same cases.
What to evaluate
Evaluate the product behavior, not just the model output. In RAG, inspect retrieved sources and final answers. In agents, inspect tool calls, stopping behavior, and recovery from failed steps.
Common mistakes
- Waiting until production to evaluate.
- Measuring only fluency.
- Ignoring negative examples and edge cases.
- Changing prompts without regression checks.
Next decision
Define what “good” means before choosing optimizations. Evaluation should guide RAG tuning, agent design, and fine-tuning decisions.