Why Similarity Metrics Fail for LLM Evaluation
Discover why relying on similarity scores alone can hide critical failures in language model outputs. Learn to use real traces and error analysis to create effective, binary evaluations that measure actual system behavior, refusal handling, and alignment with human judgments.
Teams building LLM products often reach a point where they feel pressure to measure the quality of their products. They have observed real failures and have identified traces, and they want a way to track improvement over time. This is often the point at which generic metrics are introduced. Similarity scores, overlap metrics, and embedding distances promise a simple outcome: a single number indicating whether outputs are improving or regressing. These metrics work by comparing model outputs against reference answers, typically human-written gold standards or previously labeled examples deemed acceptable.
In practice, these metrics frequently mislead. They reward surface resemblance rather than correct behavior, and they stay high even when a system makes decisions that would seriously harm users. The most reliable evaluations come from understanding how a system fails in real-world traces, then designing checks that directly reflect those failures. The sections below address three common questions that arise at this stage, along with how experienced teams approach them in practice.
Are similarity metrics useful for evaluating LLM outputs?
Generic similarity metrics often measure the wrong thing in LLM ...