For most business AI projects that need to answer questions from company information, retrieval-augmented generation (RAG) is the right starting point: it keeps answers grounded in current documents, can cite sources and updates as soon as content changes. Fine-tuning changes how a model behaves, such as its tone, format or handling of a narrow task, but it is a poor way to teach facts that change. Many good systems use RAG for knowledge, careful prompting for behaviour, and fine-tuning only when those are not enough.
What each does
| RAG | Fine-tuning | |
|---|---|---|
| How it works | Retrieves relevant passages at question time and gives them to the model | Further trains the model on examples so its behaviour changes |
| Best for | Answering from documents, policies, product data, website content | Consistent format, style, classification, specialised narrow tasks |
| Updating knowledge | Update the documents; answers change immediately | Retrain with new examples |
| Citations | Natural: answers point to retrieved sources | Not built in |
| Hallucination risk | Lower, if the model is told to answer only from sources | Can still state outdated or invented facts confidently |
| Cost to start | Indexing content and a retrieval layer | Preparing quality training data and training runs |
When to use RAG
- A website or WhatsApp assistant answering from your pages; see RAG chatbots and WhatsApp chatbots.
- Internal assistants for policies, manuals and documentation
- Any case where answers must be current, traceable and correctable
When fine-tuning can help
- Output must follow a strict format every time, and prompting alone is unreliable.
- A high-volume, narrow task, such as classifying support tickets, where a smaller fine-tuned model is cheaper and faster.
- A consistent brand voice across large volumes of short text, after prompting has been tried.
Try this order
- Clear instructions and a few examples in the prompt.
- RAG for any knowledge the model needs.
- Structured output features, such as requiring JSON, where available.
- Fine-tuning only if an evaluation shows the first three are not enough.
Measure each step on a fixed set of real test questions, so you know whether a change helps.
Common mistakes
- Fine-tuning on company documents to “teach it our business”, then finding it answers confidently with outdated facts
- Skipping evaluation and judging by a few demo questions
- Poor source content: RAG is only as good as the pages it retrieves. Unclear or outdated pages produce unclear answers.
The last point links AI projects and website quality: the same clear, answer-first, current content that helps search and AI assistants also makes your own AI tools better.
Frequently asked questions
Can RAG and fine-tuning be combined?
Yes. A fine-tuned model can be used in a RAG system, for example to follow a strict answer format while facts come from retrieval.
Does fine-tuning keep our data private?
That depends on the provider’s terms and where training happens. Review data handling for either approach, and comply with the DPDP Act, 2023 for personal data.
How much data does fine-tuning need?
It depends on the task and provider; quality matters more than quantity. Start with RAG and prompting, which need no training data.
Sources
- Lewis et al., “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks”, 2020
- Anthropic and OpenAI documentation on prompting, retrieval and fine-tuning