llm-eval-scaffolder

Automate LLM prompt evaluation in CI by generating structured test cases, running an LLM-as-judge script to score responses, and executing the pipeline via GitHub Actions to catch regressions on code changes.

An LLM-as-judge pipeline prevents prompt regressions by scoring new prompt outputs against a defined rubric using structured test cases, calculating pass/fail rates before production deployment.

Yes, you need the Anthropic API along with Python to execute the automated LLM evaluation pipelines and calculate scoring metrics for tasks like summarization and classification.

The best way to test prompt quality is generating diverse test cases, including edge and adversarial scenarios, then evaluating responses with an LLM-as-judge to ensure new prompts do not degrade critical interactions.

Yes, you can integrate GitHub Actions to automatically run LLM evaluations on code changes, ensuring that prompt updates are continuously scored against structured test cases to prevent quality degradation.

Comprehensive prompt evaluation requires structured test cases including golden, edge, and adversarial scenarios to thoroughly assess response quality across summarization, classification, and generation tasks.