LLM Evaluation: How to Measure Production AI Quality

Generative AI is probabilistic, so traditional pass-or-fail testing is not enough. A useful evaluation program combines deterministic checks, model-based scoring, expert review and live operational measures.

Build a representative test set

Collect common, difficult and high-risk examples from the actual workflow. Include incomplete inputs, conflicting evidence and requests the system should refuse. Tag examples so results can be analysed by scenario rather than hidden in one average score.

Define quality in plain language

Write rubrics for accuracy, completeness, groundedness, tone and policy compliance. Each criterion should explain what good and unacceptable output looks like so reviewers and automated judges assess the same thing.

Evaluate every meaningful change

Run the suite when prompts, models, retrieval settings, tools or source content change. Compare quality, latency and cost together; a slightly stronger answer may not justify a large operational penalty.

Monitor what tests cannot predict

Production monitoring should capture user corrections, escalations, refusals, tool failures and new question patterns. Add important failures back into the test set so evaluation improves with the system.