Building eval systems that improve your AI product

Building a robust evaluation system for AI products requires moving beyond vanity metrics toward a process grounded in systematic error analysis. Start by designating a domain expert to perform open coding on user interactions, identifying specific failure modes rather than relying on generic scores like hallucination or toxicity. Once failure modes are prioritized, implement a dual-track evaluation suite: use deterministic code-based checks for objective failures and develop an "LLM-as-a-judge" for subjective qualities like tone or relevance. Validate these judges against human-labeled ground truth using binary pass/fail judgments to ensure trust. For complex architectures like RAG or agentic workflows, isolate components—such as retrieval recall or transition steps—to pinpoint exact failure points. This rigorous, data-driven approach transforms evaluation from a passive dashboard into a continuous improvement flywheel that catches regressions before they reach users.