Advanced RAG Evaluation: Faithfulness, Context Precision, and LLM-Judge Bias

RAG hallucinates differently from raw LLMs: the model fabricates content the context did not support, or the context did not contain the needed information and the model confabulated to fill the gap. Either failure looks identical in a single accuracy score. Senior engineers running RAG in production cannot ship without a four-axis eval that pinpoints which sub-system (retrieval, generation, prompt) is failing — and they cannot trust LLM-judge scores without controlling for the well-documented j

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