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How do you measure faithfulness (hallucination rate) in a RAG system, and what makes it hard to score?

An answer can be correct and still unfaithful, stating things the retrieved context never said. The signal is scoring faithfulness against the context, not against truth, and knowing why LLM-judge faithfulness scores drift.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

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LEARN THE BACKGROUND

These lessons teach the material this question tests, in order and from the beginning.

Applied AI Engineering·EvaluationSign in14mBuilding a set of examples worth trustingYour evaluation is only as good as the examples in it, and most sets are quietly useless because of how they were assembled. This lesson covers where to get examples, what proportion should be awkward, and the maintenance nobody plans for.Applied AI Engineering·EvaluationSign in14mLetting a model grade the work, and how it lies to youUsing a model to score output is the only way to evaluate open-ended work at any scale, and it has five documented biases that will flatter you if you do not design around them. This lesson covers each one and the cheap defence.

UP NEXT ON YOUR JOURNEY

Next in this trackContext precision versus context recall: which do you optimize, and how do they trade off in RAG?Next in this trackHow do you tune the fusion weights between lexical and vector retrieval, RRF k versus a learned alpha?Next in this trackWhen do you fine-tune a reranker on your own data, and how do you build the training set?

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