RAG vs Fine-Tuning: Which Should You Use?
Know when to reach for RAG, when for fine-tuning, when for both, and when a better prompt is enough.
Teams adding their own knowledge to an AI often face a fork in the road: build RAG, or fine-tune the model on their documents? They sound like rivals. In fact they solve different problems.
RAG changes what the model knows for each question, by handing it the right pages. It shines when facts change often (just update the document and re-index), when answers must cite a source, when information is private or permission-controlled, and when the collection is far too large to memorise.
Fine-tuning changes how the model behaves, by training it on examples (Track 2, including cheap LoRA adapters). It shines for a consistent style, tone or format across thousands of replies; a specialised skill or vocabulary; or shrinking a long, repeated prompt into the model itself so every request is shorter and cheaper.
What fine-tuning is bad at, despite what many people expect, is reliably adding facts. Train a model on your 500-page policy and it will pick up the style and some of the content, but recall rare details blurrily, just as in pre-training. It can’t tell you where an answer came from, and the day the policy changes, it’s wrong until you train again.
So the usual advice runs in order of effort. First, write a better prompt; it fixes more than you’d think. Add RAG when the model needs knowledge it doesn’t have. Fine-tune when the behaviour still isn’t right after that. And for demanding systems, use both: a fine-tuned model that speaks your format, fed by RAG with current facts.
That completes the knowledge toolkit. Next, Track 7 gives the model hands as well as a library: tools it can call and actions it can take.
Key takeaways
- RAG changes what the model knows per question: fresh, private, citable facts.
- Fine-tuning changes how it behaves: style, format, specialised skills, shorter prompts.
- Fine-tuning is poor at reliably adding facts: blurry recall, no sources, stale when things change.
- Try a better prompt first, add RAG for knowledge, fine-tune for behaviour, or combine them.
Check your understanding
Go deeper (more technical)
Research comparing the two for knowledge-heavy tasks has generally found RAG better at injecting new facts, with fine-tuning helping most when combined with retrieval (for example “RAFT”, 2024, which fine-tunes a model specifically to make good use of retrieved documents, including ignoring distracting ones).
A third option has grown with context windows: simply put the whole document collection into the prompt when it is small enough, often with prompt caching (Track 5). For a few dozen pages that can beat a RAG pipeline on simplicity; for thousands of documents, retrieval is still essential.