RAG vs fine-tuning: choosing the right approach
RAG and fine-tuning solve different problems. Here's a practical framework for choosing between them — or combining both.
Different problems, different tools
RAG grounds a model in your data at inference time. Fine-tuning bakes knowledge into the model weights. They're not competing approaches — they're complementary tools for different problems.
When RAG wins
RAG is the right choice when your data changes frequently, when you need citations and provenance, or when you need to control what the model knows. It's cheaper to update and easier to debug.
When fine-tuning wins
Fine-tuning is the right choice when you need to change the model's behavior — not just its knowledge. If you need a specific tone, format, or reasoning style, fine-tuning can be more effective than prompt engineering.
Combining both
In production, the best systems often use both: a fine-tuned model for behavior, with RAG for current knowledge. This gives you control over both what the model knows and how it responds.
Let's build something intelligent.
From the first idea to a production-ready system.