AI quality cannot be represented by one universal accuracy score. A useful evaluation program connects model behavior to the tasks users need to complete and the risks the product must control.

Define Quality in Product Terms

Describe the behaviors that make an answer or action useful: factual correctness, completeness, appropriate tone, grounded evidence, tool selection, latency, or safe refusal. Weight them according to the product context.

Build a Representative Test Set

Include common requests, difficult edge cases, known failures, ambiguous inputs, and situations that should be escalated. Preserve enough context to reproduce how the system behaves in production.

Combine Automated and Human Review

Use deterministic checks where possible and model-based scoring where it is reliable. Reserve expert judgment for qualities that depend on context, then calibrate reviewers with examples and clear scoring guidance.

Run Evaluations on Every Meaningful Change

Prompts, models, retrieval settings, tools, and application code can all change behavior. Compare proposed versions against a baseline before release and investigate improvements as carefully as regressions.

Feed Production Evidence Back Into Testing

Turn user feedback, escalations, and observed failures into new evaluation cases. The test set should evolve with the product so quality work remains connected to real use rather than a static benchmark.