RAG vs Fine-Tuning for Business AI

What to assess Retrieval-augmented generation, or RAG, supplies relevant external documents when the user asks a question. It is often a good fit for changing company knowledge.

How to apply it Fine-tuning changes a model’s behavior through additional training examples. It can help with consistent format or a specialized task, but it is usually not the simplest way to keep facts current.

Operational considerations Start with a small evaluation set of real questions and known good answers. Compare quality, latency, cost, update effort, and privacy for each approach.

Next step Both approaches can produce errors. Use source review, monitoring, and human escalation where an incorrect answer could cause harm.