Your Hallucination Rate Is a Vanity Metric

You have shipped a RAG pipeline, You have evals running, Your hallucination rate is sitting at 12%, and you have spent the last three weeks trying to push it below 10%, You have tweaked chunk sizes, switched embedding models, bumped top-k from 3 to 5, tried reranking. The number barely moves.

Here is the thing nobody tells you: you might be optimizing for a metric that does not mean anything.

A hallucination rate with no type information is like a server monitoring dashboard that shows one metric: errors: true. Technically accurate. Operationally useless. You would not ship a backend without distinguishing 4xx from 5xx, timeouts from connection resets, OOM from bad queries. But that is exactly what most LLM evaluation pipelines do with hallucinations.

Not all hallucinations are the same failure

Let me show you what I mean with three outputs from the same RAG pipeline, same model, same day.

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