Louis Bouchard

Accuracy Isn’t Enough: Prioritize Relevance, Grounding, Faithfulness

Accuracy is a terrible metric for LLMs. And it’s the reason many AI demos look great but fall apart in real usage. LLMs don’t usually fail by being wrong. They fail by being: irrelevant ungrounded confidently misleading An answer can be “accurate” in isolation and still be useless to the user. This is why traditional evaluation breaks down. For LLM systems, what actually matters is: Relevance - did it answer this question? Groundedness - is it backed by the right context or sources? Faithfulness - did it stay true to the input data? Accuracy alone can’t measure any of that. That’s why production LLMs need evaluation that looks beyond correctness- and focuses on how answers are produced, not just what they say. If your model feels unreliable despite “good accuracy,” this is usually the reason.