Exact-match and F1 miss semantic equivalence. Human review does not scale. LLM-as-judge is the production answer — with enough calibration to trust the number.
Your RAG system answers: "June 29th, 2007."
The gold reference is: "June 29, 2007."
Exact Match scores 0. F1 scores ~75%. A human would score 100%.
Now multiply by 10,000 test cases. Multiply again by every change to the retriever, chunking, prompt, or model. You need an evaluator that understands meaning, runs cheaply at scale, does not lie about regressions, and surfaces the right failure modes.
2026 has three frameworks that own this problem.
RAGAS. Retrieval-Augmented Generation ASsessment. Four RAG metrics (faithfulness, answer-relevance, context-precision, context-recall) with NLI + LLM-judge backends. Research-backed, lightweight.
DeepEval. Pytest for LLMs. G-Eval, task-completion, hallucination, bias metrics. CI/CD-native.
G-Eval. A method (and a DeepEval metric): LLM-as-judge with chain-of-thought, custom criteria, 0-1 score.
All three lean on LLM-as-judge. This lesson builds intuition for the method and the trust layer around it.
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