Insights
RAG vs fine-tuning: which approach does your business need?
Two common ways to make a large language model useful for your business, explained in plain English, with a simple way to decide.
When a business wants an AI assistant that knows about its products, policies or documents, two approaches come up: retrieval-augmented generation (RAG) and fine-tuning. They solve different problems. Choosing the wrong one wastes time and money.
What is RAG?
Retrieval-augmented generation keeps your information outside the model. When someone asks a question, the system first searches your content (usually a search index built from text embeddings), retrieves the most relevant passages, and gives them to the model alongside the question. The model then writes an answer based on those passages.
- Best for: answering questions from documents, policies, product data or knowledge bases that change over time.
- Strengths: update the content and the answers change immediately; answers can cite their sources; access control can limit what each user can see.
- Watch out for: answer quality depends on retrieval quality. Poorly structured documents, bad chunking or missing content lead to weak answers.
What is fine-tuning?
Fine-tuning continues training a model on your own examples, changing how it behaves. It is good at teaching a consistent style, format or classification task. It is not a reliable way to store facts that change.
- Best for: consistent tone of voice, structured outputs, labelling or classification tasks, and domain-specific phrasing.
- Strengths: shorter prompts and more consistent behaviour once trained.
- Watch out for: you need a good set of high-quality examples, and updating knowledge means retraining. A fine-tuned model can still produce confident but wrong answers.
How to decide
- Does the assistant need to know facts that change? Use RAG.
- Do you need answers with sources people can check? Use RAG.
- Is the problem mainly style, format or a repeated classification task? Consider fine-tuning.
- Not sure? Start with careful prompting plus RAG. It is usually faster to build, cheaper to change and easier to evaluate.
The two are not mutually exclusive. Some systems use RAG for knowledge and a fine-tuned model for a consistent output format.
Evaluate before you launch
Whichever approach you choose, build a test set of real questions with good answers, and measure the system against it before launch and after every change. This is the most reliable way to catch regressions and to show stakeholders that the assistant is actually helping.
Privacy and data protection
Check how your AI provider handles the data you send, limit personal data where possible, and record your decisions. If you process personal data, your UK GDPR obligations still apply, so involve whoever handles data protection in your organisation early.
Want help choosing? We build RAG assistants, document Q&A and AI workflows. See our AI integration service.
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