Originally published on AI Tech Connect.
What you need to know The default failure of LLM evaluation in 2026 is not bad metrics — it is premature infrastructure. A team ships a RAG feature or a support agent, stands up a dashboard of generic scores (hallucination rate, toxicity, relevance), wires an eval framework into CI, and discovers weeks later that none of it maps to how the product actually fails in front of users. The fix is a method popularised by Hamel Husain and Shreya Shankar — through Husain's writing on evals and the pair's widely cited AI evals FAQ (updated January 2026) — called error analysis, and it inverts the usual order of work. You read your production traces first, write down what went wrong in plain language, count the failures, and only then automate evals for the failure modes that actually occur. Their…
Top comments (0)