Prompt Evaluation and Versioning
Golden sets, LLM-as-judge scoring, CI regression tests, A/B testing, and rollback — the production workflow that separates a shipped prompt from a playground experiment
The full production workflow for treating prompts as code: curating golden sets from real traffic, the scoring ladder from exact match to LLM-as-judge (and the judge biases that quietly wreck it), running evals as CI gates on every prompt diff, why offline evals lie and what online A/B testing catches that they miss, versioning and rollback through a prompt registry, and the tracing/observability layer that tells you a regression shipped before support does.
Practice questions (5)
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Building a Golden Set From Scratch for a Refund-Request Classifier
Intermediate · Free -
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Diagnosing a Suspiciously Confident LLM-as-Judge
Intermediate -
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Setting a CI Regression Threshold That Doesn't Cry Wolf
Intermediate -
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A/B Test Looks Like a Win — Until You Check the Guardrail Metrics
Intermediate -
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A Metric Regressed but the Prompt Diff Is Clean — Now What?
Intermediate