Model Evaluation and Monitoring

Model evaluation and monitoring provide confidence that AI behavior is useful, safe, and stable enough for the workflow. They should be part of release and operations, not a one-time notebook.

Evaluation types

Use multiple evaluation methods:

Golden datasets for known scenarios.
Regression tests for previously fixed failures.
Human review for judgment-heavy outputs.
Automated scoring for structured outputs.
Red-team prompts for abuse and safety cases.
Production sampling where privacy allows it.

Evaluation flow

Metrics

Track metrics that match the product risk:

Task success rate.
Groundedness or citation accuracy.
Refusal quality.
Toxicity or policy violation rate.
Tool call success and failure rate.
Latency and timeout rate.
Cost per task or user journey.
Escalation or human correction rate.

Monitoring

Production monitoring should include:

Model, prompt, retrieval, and tool versions.
Latency, errors, retries, and provider failures.
Token usage and cost.
Safety events and blocked actions.
Drift in input mix or retrieval quality.
User feedback and correction signals.

Watchouts

A high aggregate score can hide critical failures in small segments.
Evals become stale as product behavior changes.
Human review needs rubrics or reviewers will disagree silently.
Do not store sensitive prompts or responses unless approved.