Fine-tuning isn't a replacement for RAG – it's something else
Fine-tuning means retraining an existing language model with your own examples until it "picks up" the desired behavior. Unlike RAG, it's not about supplying the model with knowledge – it's about teaching it a particular way of working.
The three cases where fine-tuning really helps
A fixed style or format
Answers need to reliably come out in a particular tone or format – say, always as a structured response in a fixed layout instead of free-flowing text.
Specialized jargon
A very narrow field with its own terminology, where prompting alone hits its limits.
Very high request volume with a narrow pattern
At millions of similar requests, a smaller, fine-tuned model can be cheaper and faster than a large model carrying a long RAG context on every single request.
Where fine-tuning is NOT the answer
"The AI should know our company knowledge" is almost always the wrong, too-expensive, and too-rigid answer with fine-tuning (see module 15): new knowledge means retraining, and a model doesn't selectively "forget" what's outdated.
Why this matters for you as a decision-maker
When a vendor suggests fine-tuning, it's worth asking first: is this about knowledge (then RAG) or about style, format, or very high, standardized volume (then maybe fine-tuning)? This mix-up is the single most common source of wasted time and budget in practice.