Evaluation, Testing, and Workflow Integration
Evaluation design
Build a representative evaluation set-often 30–100 cases-with clear pass criteria before tuning prompts. Include easy, ambiguous, and adversarial cases. Use exact-match or rubric scoring for deterministic tasks, and calibrated LLM-as-judge or human review for open-ended tasks. Keep a held-out set to avoid overfitting. Track accuracy, latency, cost, and failure modes together; a prompt that improves accuracy but doubles latency may not ship.
Workflow integration
Version prompts and model choices in source control, run regression tests in CI on every change, and canary new versions in production. Log inputs, outputs, model versions, and request IDs for observability. Collect user feedback and failure cases into the eval set. Offline evals catch regressions; online A/B tests catch distribution shift.
Traps
The biggest trap is judging from one anecdote or a handful of cherry-picked examples. Another is using an LLM judge without validating it against human labels; judges can share the same biases as the model under test. Do not compare a new prompt against no baseline, and do not tune on the test set. Also avoid optimizing a single metric while ignoring cost or safety.
Worked example
Suppose a new prompt raises JSON validity from 88% to 96% but latency rises 40%. Run the full regression suite, check high-stakes cases, and canary on 5% of traffic before full rollout.
Rule of thumb
No prompt or model change ships without passing the same eval set that defined the baseline.