Teams that ship LLM features without an eval suite eventually get burned by a prompt change that silently breaks a critical workflow. Evals are how you catch that before users do.
What an eval suite actually is
An eval suite is a collection of representative inputs paired with a way to judge whether the output was acceptable. It is not a single accuracy number — it is a structured test bed you run on every meaningful change.
Start with real task examples
Pull examples from actual usage, not invented cases:
- Common, everyday requests (the happy path)
- Ambiguous requests where intent is unclear
- Edge cases that caused problems before
- Adversarial or unsafe inputs the system must refuse or handle safely
Aim for at least 30-50 cases per critical workflow before you trust the results.
Choose a scoring approach
Common scoring methods, from simplest to most involved:
- Exact match or rule-based checks for structured outputs
- Human-labeled pass/fail with clear rubrics for subjective quality
- Model-graded evaluation, where a second model scores outputs against a rubric
- Task-success scoring, where you check if the end goal was actually achieved
Model-graded eval is convenient but should be spot-checked against human judgment periodically, since evaluator models can drift too.
Set pass thresholds before you look at results
Decide in advance what "good enough to ship" means. If you decide after seeing the numbers, you will unconsciously lower the bar to justify shipping.
Wire evals into your release process
Treat the eval suite like a test suite:
- Run it automatically on prompt or model changes
- Block release if scores drop below your threshold
- Track score trends over time, not just pass/fail at one point
This is the same discipline behind Agent Ops rollback practices. See our Agent Ops checklist.
Common mistakes teams make
- Testing only the happy path
- No versioning on the eval set itself, so comparisons across runs are meaningless
- Treating eval scores as a one-time gate instead of an ongoing signal
- Ignoring cost and latency as part of "acceptable" output
Related reading
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