AI Prompt Engineering Log Template

Prompts are code. A prompt change can break user experience, introduce [hallucinations](/glossary/hallucination), or triple your API costs. Version control gives you rollback capability, audit trails, and the ability to correlate production incidents with specific prompt changes. Teams that do not version prompts spend hours debugging issues that a version diff would explain in seconds.

Both. Store the canonical prompt text in source control (Git) for deployment and rollback. Use this log to document the reasoning, test results, and production impact that source control diffs cannot capture. Link between them using version numbers.

Route a percentage of traffic (start with 5-10%) to the new prompt version while the rest uses the current version. Measure the same metrics tracked in your test suite (accuracy, hallucination rate, user satisfaction). Run the test for at least one week to account for input variation. Only promote the new version if it improves target metrics without degrading others.

This is why you have a test suite. Run your full test suite whenever your model provider ships an update. If tests fail, the prompt needs adjustment for the new model version. Log it as a new prompt version with "Model Update Compatibility" as the change type. The [AI PM Handbook](/ai-guide) covers strategies for managing model provider dependencies.

There is no fixed limit, but if you are shipping more than 2-3 prompt versions per week, you may be optimizing prematurely or reacting to individual complaints instead of systematic issues. Focus prompt changes on measurable improvements to your core metrics. Archive old versions but keep the log for reference.