When tuning AI models, particularly Retrieval-Augmented Generation (RAG) systems, many teams focus either overindex or confuse accuracy and precision. This fundamental misunderstanding leads to systems that either hit near the mark occasionally but scatter wildly in practice, or consistently miss the mark (or hallucinate) in the same way repeatedly.