Offline Evals Before User Traffic

Offline evaluation lets you compare prompt/model variants before rollout - the AI equivalent of a test suite that must pass before deploy. Without it, every prompt tweak is an uncontrolled experiment on your users.

Dataset Design

Scoring Methods

• Programmatic - JSON validity, schema adherence, exact-match classification labels. Cheap, deterministic, run on everything.
• LLM-as-judge - a strong model scores factuality, instruction adherence, and tone against a rubric. Calibrate the judge against a sample of human ratings before trusting it.
• Human review - reserve for high-stakes samples and judge calibration; too expensive for every run.

Amazon Bedrock Evaluations supports automatic metrics, LLM-as-judge, and human evaluation workflows natively - including RAG-specific evaluation for Knowledge Bases (retrieval quality vs generation quality scored separately).

Gates, Not Vibes

# promotion gate example
release if:
  accuracy   >= 0.85   (golden set)
  safety     >= 0.97   (adversarial block rate)
  schema_ok  >= 0.99   (programmatic)
  p95_latency <= 2200ms
else: block promotion, file regression diff

• Use representative and adversarial datasets - passing only friendly inputs proves nothing.
• Score factuality, instruction adherence, and policy compliance as separate dimensions - one aggregate score hides which dimension regressed.
• Gate promotion with explicit pass thresholds agreed before the run.