The danger of bimodal distributions in RAG evaluation

Relying on a mean faithfulness metric of 0.75 provides a false sense of security that can lead to wasted engineering cycles. During a recent evaluation of my RAG pipeline, I spent several days attempting to optimize prompts to address perceived "drift" in the output. However, the metric was a mathematical ghost. When I finally plotted the raw distribution, it wasn't a bell curve; it was a bimodal distribution with one cluster at 0.95 and a failure cluster near 0.05.

The central issue is that LLM evaluation metrics for faithfulness and correctness do not behave like smooth, continuous variables. They tend to clump at the extremes—the model is either grounded in the provided context or it is hallucinating entirely. When you force these binary realities into a single decimal summary, you create an "average" that describes a state where no actual data exists. This is a classic case of Anscombe's Quartet applied to AI metrics: two datasets can share the same mean and variance while possessing fundamentally different underlying structures.

From a maintenance and reliability perspective, a 0.75 score is deceptive. It doesn't indicate a model that is "75% faithful." Instead, it often masks two distinct failure modes. One mode might require minor prompt adjustments, while the other likely points to systemic issues in the retrieval layer or inadequate data filtering during the ingestion phase. Treating them as a single problem results in inefficient resource allocation.

To ensure better observability and compliance with expected performance bounds, you must move beyond summary statistics. Before finalizing any test run report, generate a histogram of the scores. If the histogram shows two peaks with a hollow center, your median is stranded in a valley and your mean is lying to you. Always inspect the percentiles and the distribution shape to identify where the failures are actually occurring.

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