eval-driven-development

To prevent prompt regressions during LLM feature development, adopt an eval-first approach by writing evaluations before modifying prompts, establishing baseline scores to measure performance, and testing incremental changes to isolate their impact.

Eval-driven development is a systematic approach for LLM features that prioritizes writing evaluations before making prompt or model adjustments, ensuring feature reliability by mitigating the risk of regressions through systematic baseline performance testing.

You can set up automated regression testing for LLM prompts by integrating new bugs as test cases and automating evaluation runs in Continuous Integration, which provides real-time feedback to prevent future model regressions.

Yes, you can apply test-driven development practices to LLM model adjustments by establishing baseline performance scores first, then testing incremental changes individually to isolate their impact and ensure feature reliability.

LLM feature performance degrades after prompt updates because changes often introduce regressions; establishing baseline scores and testing incremental changes one at a time isolates their impact to mitigate this reliability risk.

The best way to isolate the impact of prompt engineering changes is to test incremental changes one at a time against an established baseline score, ensuring feature reliability by preventing regressions.