Evaluation Systems for AI Applications

Evaluation Is the Engineering Feedback Loop

AI applications need evaluation systems because behavior changes with prompts, models, retrieval, tools, and user inputs. Without evals, teams cannot safely improve or deploy changes.

Core Evaluation Layers

• Golden datasets for known user tasks and expected outcomes.
• Retrieval evals for source recall, precision, freshness, and permission correctness.
• Generation evals for correctness, completeness, tone, structure, and groundedness.
• Tool-use evals for proper selection, valid arguments, and safe execution.
• Safety evals for refusal behavior, prompt injection, privacy, and policy compliance.

Use Evals Before and After Launch

Pre-launch evals catch regressions before deployment. Production feedback catches real-world failures that static test sets miss. Use both.