Prompt Versioning and A/B Testing — iterate with data
Track prompt changes, run controlled A/B tests against evaluation datasets, and deploy the best-performing version with confidence.
Optimization4.4
Prompt template
Evaluate this prompt version against the test set.
Prompt: {prompt_version}
Test cases: {evaluation_dataset}
For each test case, score output on:
- Accuracy (1-5)
- Relevance (1-5)
- Format compliance (1-5)
Report average scores and identify failing cases.How to use
- 1Create a versioned prompt registry (Git or prompt management tool)
- 2Build an evaluation dataset of 20-50 representative test cases
- 3Define scoring rubrics for each quality dimension
- 4Run A/B tests with statistical significance (p<0.05)
- 5Deploy winning version and archive the old one
Pro tips
- Use tools like PromptFoo, LangSmith, or Humanloop for evaluation
- Test with edge cases and adversarial inputs, not just happy paths
- Run at least 30 samples per version for statistical significance
- Document what changed and why in each version
A/B TestingVersioningEvaluationMLOpschatgpt
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