Two different questions
RAG answers "what does the model know?" — it gives an LLM access to specific information it wasn't trained on. Fine-tuning answers "how does the model behave?" — it changes the model's underlying patterns of response. Conflating the two leads to a lot of wasted engineering effort, usually in the direction of fine-tuning a model to "know" things that retrieval would have solved more cheaply and more reliably.
When RAG is the right call
- You need the assistant to answer questions grounded in specific, changeable documents or data
- Information needs to stay current without retraining every time something changes
- You want the system to cite sources or be auditable about where an answer came from
See What is RAG (Retrieval-Augmented Generation)? for the full explanation.
When fine-tuning is the right call
- You need a consistent tone, format, or behavior that's hard to reliably prompt your way into
- The task is narrow and repetitive enough that a smaller, specialized model outperforms prompting a general one
- You need lower latency or lower cost per request than a large general-purpose model at scale
Why real products often use both
Fine-tuning to get a model's format and tone right, combined with RAG to keep it grounded in current, specific information, is a common and often the most effective real-world architecture — the two aren't mutually exclusive, they solve different halves of the problem.
The mistake to avoid
Don't reach for fine-tuning to solve a knowledge problem — it's more expensive, slower to iterate on (every data update means retraining), and doesn't actually solve "the model doesn't know this" as reliably as retrieval does. This is the single most common over-engineering mistake we see in early AI project scoping.
Not sure which one your project needs?
This is exactly the kind of question a scoping conversation resolves quickly. See our AI Integration page for how we approach this, or tell us what you're building and we'll tell you which approach actually fits — including if the honest answer is "neither, yet."