How do you test and evaluate AI features before shipping them?

AI systems require different testing approaches than traditional software because outputs are probabilistic and subjective.

Evaluation datasets capture representative examples of your use case. Include easy cases, hard cases, and known edge cases. Store input, expected output (or acceptable output criteria), and any context. These become your regression tests.

Define measurable success criteria. For factual tasks: accuracy against ground truth. For generation: human ratings, automated metrics (BLEU, ROUGE), or LLM-as-judge approaches where another model evaluates outputs.

Test edge cases systematically: unusual inputs, adversarial prompts, very long/short inputs, different languages if applicable, and inputs that might trigger harmful outputs. Document known limitations.

Human evaluation remains essential for subjective quality. Rate outputs on scales (1-5 for helpfulness) or compare A/B outputs. Even small amounts of human feedback catch issues automated metrics miss.

Prompt regression testing: when you change prompts, run your evaluation dataset to check for regressions. Small prompt changes can significantly affect outputs.

Production monitoring tracks real-world performance. Log inputs and outputs (respecting privacy), monitor latency and error rates, sample outputs for quality review, and set up alerts for anomalies.

A/B testing in production compares changes carefully. Roll out gradually, measure user-facing metrics (task completion, satisfaction), and be prepared to roll back.