AI Evaluation & Observability Playbook
From Prototype to Live Workflow
A practical guide to evaluation cases, trustworthy graders, traces, production feedback, and release decisions from prototype to live workflow.
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Make evaluation evidence useful in production
Build task-level cases, calibrate graders, connect traces to feedback, and make release decisions with evidence.
What's covered
Inside the playbook
01
Evaluation Fundamentals
- Choose evaluation methods by development stage
- Define task outcomes and failure modes
02
Task-Level Cases
- Build cases for an account-change workflow
- Work through a RAG answer and source-grounding example
03
Trustworthy Graders
- Calibrate graders against reviewed cases
- Compare results with model, prompt, data, and grader configuration fixed
04
Production Observation and Release Evidence
- Connect traces to task outcomes and operator feedback
- Use production failures to update cases and release decisions
Next Step
Explore the related engineering path
Use this resource to sharpen the engineering decision, then explore the related review, architecture, or implementation scope.
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