Test, Monitor, and Optimize AI Solutions
AI systems fail silently, so testing must cover both deterministic behavior and probabilistic quality. Build a golden evaluation set of representative inputs with expected outputs or grading criteria. Use automated metrics where objective (exact match, schema validity, retrieval recall) and model-based or human grading for subjective qualities (helpfulness, faithfulness, tone). Track quality per category, not just an aggregate average that hides regressions.
Regression testing runs the eval set on every prompt, model, or retrieval change. Version prompts and configurations alongside code so changes are attributable. Include adversarial and edge cases: empty input, very long input, prompt injection attempts, out-of-scope requests, and ambiguous queries.
Production monitoring covers latency, error rates, token usage and cost, tool-call failures, and quality signals from user feedback or sampled review. Log prompts and responses with appropriate redaction and retention. Define alert thresholds on both system health and quality drift.
Optimization levers: prompt refinement, retrieval tuning, caching, smaller models for easy routes, batching, and streaming for perceived latency. Optimize against the metric you defined, not vibes.
Traps:
- Reporting only an average score and missing a category regression.
- Changing the model without rerunning evals.
- Logging sensitive data without redaction.
- Treating a passing eval set as proof of general correctness.
Rule of thumb: no change ships without an eval run and a recorded baseline. Worked example: after switching models, rerun the golden set; if faithfulness improves but latency doubles, route simple queries to the smaller model and keep the larger one for complex cases.