Eval-Driven Development
Iteratively improving AI systems based on evaluation (Eval)
Eval-Driven Development applies TDD (Test-Driven Development) philosophy to AI system development. When modifying prompts, switching models, or improving RAG pipelines, decisions are made with quantitative evaluation metrics rather than intuition. Evaluation datasets (question-answer pairs) are prepared, and automated Eval pipelines measure accuracy, relevance, harmfulness, etc. LLM-as-a-Judge (GPT-4 evaluating response quality) is also widely used. Tools like Braintrust, Langsmith, and Humanloop support this workflow.
Key Concepts
Build evaluation dataset: input (question) + expected output (answer/criteria) pairs
Define evaluation criteria: accuracy, relevance, harmfulness, format compliance scoring
Run evaluation on current system โ measure baseline performance
Re-run same evaluation after prompt/model/RAG changes
Compare performance: determine if change is improvement or regression using data
Iterate: evaluate โ improve โ re-evaluate cycle
Pros
- ✓ Objective data-driven decision making
- ✓ Prevents regression
- ✓ Consistent quality standards across teams
- ✓ Can be integrated into CI/CD pipelines
Cons
- ✗ Difficult to build good evaluation datasets
- ✗ Possible bias in LLM-as-Judge
- ✗ Evaluation costs (large volume of LLM calls)
- ✗ Some quality factors are hard to quantify