RAG vs fine-tuning. Which one?
A clear comparison of the two ways to specialise a language model, from cost and freshness to hallucinations, so you pick the right one, or both.
ZENKEI
ZENKEI
A clear comparison of the two ways to specialise a language model, from cost and freshness to hallucinations, so you pick the right one, or both.
Use RAG when the model needs current, factual knowledge from your own documents, with citations. Use fine-tuning when it needs a specific behaviour: a tone, a format, or a domain skill. In one line: RAG changes what the model knows, fine-tuning changes how it behaves. They are not rivals, and many production systems use both.
Both are ways to make a general language model work for your use case, but they solve different problems. RAG feeds the model relevant knowledge at answer time; fine-tuning bakes new behaviour into the model itself. Here is how they compare on the things that decide a project.
| Dimension | RAG | Fine-tuning |
|---|---|---|
| What it changes | What the model knows | How the model behaves |
| Best for | Current, factual knowledge | Tone, format, domain skills |
| Updating | Change the documents, instantly | Retrain the model |
| Upfront cost | Lower, no training | Higher, training compute |
| Running cost | Vector database and retrieval | Hosting the tuned model |
| Hallucinations | Reduced by grounding and citations | Can still hallucinate facts |
| Explainability | Answers cite their sources | Opaque, no citations |
| Privacy | Can run fully private | Can run fully private |
| Best when | Knowledge changes often | Behaviour must be consistent |
RAG, retrieval-augmented generation, connects a model to your own documents. It retrieves the passages relevant to a question and asks the model to answer from them, with citations. Because the knowledge lives in your documents rather than the model's weights, you update it by editing documents, and every answer can be traced to a source. It is the right tool when facts matter and change over time. For the mechanics, see our guide on how to build a RAG pipeline.
Fine-tuning continues training a base model on your examples, adjusting its weights so it writes and reasons the way you want. It is excellent at teaching a consistent tone, a strict output format, or a specialised skill, and it can shrink prompts and latency for a fixed task. What it is not good at is storing large amounts of factual knowledge reliably, which is why it pairs so well with RAG. See custom LLM development for how we approach it.
Yes, and it is often the best answer. A common production pattern is to fine-tune a model for tone, structure and domain skill, then use RAG to give it current, factual knowledge with citations at answer time. You get behaviour from fine-tuning and knowledge from RAG, each doing what it is best at. Most real systems that feel polished are doing exactly this.
We help you decide and build it. See RAG development for knowledge systems, custom LLM development for fine-tuning, or explore the full AI hub. Still weighing whether to build at all? See build vs buy AI.
RAG changes what a model knows by retrieving your documents at answer time, so it is best for current, factual knowledge with citations. Fine-tuning changes how a model behaves by adjusting its weights on examples, so it is best for tone, format and domain skills. RAG updates knowledge; fine-tuning updates behaviour.
Usually yes to start. RAG avoids training cost and lets you update knowledge by changing documents, at the price of running a vector database and retrieval. Fine-tuning has upfront training cost and needs retraining to update, but can reduce prompt size and latency for a fixed behaviour.
Only weakly and unreliably. Fine-tuning is good at teaching behaviour and format, not at storing large amounts of factual knowledge you can trust. For current, verifiable facts, RAG is the better tool.
Yes, and many production systems do. A common pattern is to fine-tune a model for tone, structure and domain skill, then use RAG to feed it current, factual knowledge with citations at answer time.
RAG. A chatbot that answers from your own documents needs current, verifiable knowledge with citations, which is exactly what RAG provides. Add light fine-tuning only if you also need a very specific tone or output format.
Tell us the problem. We pick RAG, fine-tuning or both, and build it for production.