In this article
Problem: AI features are shipped without measurable quality benchmarks. When models degrade or prompts change, there is no way to detect regressions.
Solution: Before implementing a new AI feature, define and implement: what does "good" look like, how will we measure it, and what threshold is acceptable. Treat evals as first-class engineering artifacts.
EDD workflow:
- Define quality dimensions: For a RAG feature — faithfulness, relevance, completeness, latency
- Build eval dataset: Curate 20-100 representative test cases with expected outputs (or reference answers)
- Choose metrics: RAGAS scores, LLM-as-judge rubrics, exact match, BLEU, human preference
- Set thresholds: e.g., "Faithfulness > 0.85, Relevance > 0.80, P95 latency < 3s"
- Run in CI: Eval suite runs on every prompt or model change; fail the PR if thresholds are not met
- Monitor in production: Sample 5% of live requests and evaluate continuously
Tools: RAGAS, DeepEval, PromptFoo, OpenAI Evals, LangSmith Dataset + Evaluation
Trade-Offs:
- ✔ Pro: Regressions in model or prompt quality are caught before users see them
- ✔ Pro: Forces clarity on what "good AI" means for each feature
- ✖ Con: Building high-quality eval datasets is expensive and time-consuming
- ✖ Con: Automated metrics don't perfectly correlate with human quality perception
When To Use: Every production AI feature. Non-negotiable for customer-facing AI. When to avoid: Rapid prototyping and experimentation phases (but switch to EDD before production).