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.

In today’s data-rich business environment, leaders often feel pressured to choose between two seemingly opposing approaches: follow the data or trust their gut. This perceived conflict misses a crucial insight—the most effective decision-makers don’t choose one over the other, they blend both in a complementary fashion.