Author(s): Anubhav

Originally published on Towards AI.

Five questions, five papers, five things your RAG eval is probably getting wrong.

If you have built a RAG, you have asked yourself the question: is this thing actually any good?

After the lead paragraph, the article explains that RAG evaluation must measure more than just plausibility, because systems can be confidently wrong; it lays out a five-question framework that covers (1) whether the retriever returns the right context (using BEIR-style IR metrics like Recall@k and nDCG@k), (2) whether the generated answer is faithful and relevant to that context (using RAGAS-style faithfulness and answer/context relevance metrics rather than single aggregates), (3) whether LLM-based judges produce trustworthy scores (highlighting ARES/G-Eval and the need for calibration with Prediction-Powered Inference to get defensible error bars), (4) whether the system handles missing, noisy, or contradictory context (using RGB-style axes such as negative rejection, noise resilience, and information integration), and (5) how often hallucinations actually happen and how to detect them (using HaluEval/HaluLens recognition-style validation plus domain stress sets and tracking refusal vs hallucination rates over time). It then concludes with a “what not to measure” section warning against single-score dashboards, overlap-based metrics like BLEU/ROUGE, happy-path-only test suites, uncalibrated LLM judges, and benchmark-only evaluation, arguing that true evaluation requires these five axes to avoid false confidence at ship time.

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Published via Towards AI