LangChain shares mental models for high-quality LLM evals from harmonic's Austin Berke
LangChain · x · 2026-11-06
LangChain highlights an article on 'anthropomorphic evals' by Austin Berke and the harmonic team, sharing mental models for writing high-quality LLM evals: balancing tradeoffs across eval dimensions instead of optimizing a single score, evaluating verifiable correctness rather than prescribing one 'right' answer, and manually inspecting data to reward responses that build 'trust' — a quality often missed when verification is left entirely to a language model.
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