RAG-Based Testing Series — Part 3: Faithfulness & Hallucination Detection

Dev.to / 6/11/2026

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Key Points

The article explains that even with a “perfect” retriever (high Precision@K, Recall@K, and MRR), a RAG system can still produce incorrect answers because the LLM may ignore or misuse the retrieved context.
It describes hallucination as a generation-layer issue rather than a retrieval-layer one, highlighting that the model can either disregard context, partially use it while inventing missing parts, or directly contradict the provided documents.
It distinguishes two types of hallucination in RAG systems: intrinsic hallucination, where the LLM output directly contradicts the retrieved context, and extrinsic hallucination, where the LLM invents information not present in the retrieved context at all.
The piece positions “faithfulness & hallucination detection” as a separate testing strategy from retrieval-quality testing, motivated by real-world production failures that retrieval-focused tests won’t catch.

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