AI Development6 min read
RAG vs fine-tuning: how to choose the right approach for your business AI
When a business wants an AI assistant that knows its policies, products or procedures, two techniques come up: retrieval-augmented generation (RAG) and fine-tuning. They solve different problems, and choosing the wrong one is a common reason AI projects disappoint.
What each approach does
RAG keeps the language model as it is and gives it the right information at the moment of the question. Your documents are indexed; when someone asks something, the most relevant passages are retrieved and supplied to the model, which answers using them and can cite its sources.
Fine-tuning changes the model itself by training it further on examples. It is good at teaching a consistent style, format or narrow behaviour, but it is not a reliable way to teach a model facts that change.
When RAG is the better choice
For most company knowledge use cases, RAG is the practical starting point:
- Your information changes: policies, prices and product details can be updated by re-indexing documents.
- You need answers grounded in sources, with citations people can check.
- Different users should see different content, enforced through permissions on retrieval.
- You want to start quickly with the documents you already have.
When fine-tuning earns its cost
Fine-tuning makes sense for narrower, stable tasks:
- Producing output in a very specific format or tone at scale.
- Classification or extraction tasks with many labelled examples.
- Reducing prompt length and cost for a high-volume, repetitive task.
Often the answer is both, or neither
Some systems combine a lightly tuned model with retrieval. And sometimes the right answer is neither: a well-designed rule-based automation can be cheaper and more predictable than any AI. Whatever the approach, testing against real examples and adding human review where mistakes are costly matter more than the technique.