Most teams reach for fine-tuning first because it feels like the "real" machine learning approach. In practice, for enterprise assistants grounded in internal knowledge, retrieval-augmented generation (RAG) wins far more often - it's cheaper to maintain, doesn't require retraining when policies change, and keeps a clear audit trail back to source documents.
Fine-tuning earns its place when you need the model to adopt a very specific tone, format, or reasoning style that retrieval alone cannot teach - think legal drafting in a house style, or classification tasks with thousands of labeled examples.
Our rule of thumb: start with RAG, measure retrieval quality first, and only fine-tune once you've proven the knowledge layer is solid but the behavior layer still falls short.
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