LLM Evaluation Program Playbook
A four-phase program to build LLM evaluation: an eval strategy and reference datasets, offline automated and LLM-as-judge evals calibrated to humans, online A/B measurement, and regression gating in CI.
LLM Evaluation Program Playbook
Evaluations (evals) are how you know whether an LLM system is good and whether changes help or hurt. This playbook builds a rigorous evaluation capability spanning offline evals, online metrics, and regression gating in CI. It suits teams shipping LLM features who currently rely on spot checks.
Phase-by-Phase
Eval Strategy. Define the quality dimensions that matter: accuracy, helpfulness, safety, and format. Build reference datasets that represent real usage. Select eval methods, from exact match to model-graded scoring.
Offline Evaluation. Implement automated evals against the reference datasets. Add LLM-as-judge scoring for open-ended outputs. Calibrate judges against human ratings so scores are trustworthy.
Online Measurement. Instrument production metrics tied to user outcomes. Run A/B tests behind feature flags to measure real impact. Collect user feedback as an ongoing signal and watch for drift.
Regression Gating. Gate releases on eval thresholds so quality cannot silently regress. Track quality over time. Automate evals in CI as part of the deployment pipeline.
Team and Roles
Data engineers build the eval harness and datasets. An architect designs how evals fit the pipeline. QA owns regression gates. A product owner defines quality dimensions and reviews trends.
Risks and Mitigations
- Unreliable judges give misleading scores; mitigate with human calibration and spot audits.
- Dataset staleness as usage shifts; mitigate by refreshing reference sets from production samples.
- Metric gaming where changes optimize the eval not the user; mitigate by pairing offline evals with online metrics.
Success Criteria
Track eval coverage, human agreement with automated scores, and regression catch rate. Success means trustworthy evals that reliably catch regressions before release.
Tooling
Python builds the eval harness. GitHub Actions runs evals as CI gates. Datadog captures production LLM metrics. PostgreSQL stores datasets and results.