Comparison
RAG vs fine-tuning
Last reviewed , published by Gjethe
Retrieval-augmented generation (RAG) gives an AI model access to your documents at answer time, so it can respond with current, specific information and cite sources. Fine-tuning trains a model further on examples to change its behaviour, style or performance on a specific task. For answering questions from company knowledge, RAG is usually the better choice; fine-tuning suits consistent formats, specialised tasks or style.
RAG
Fine-tuning
RAG vs fine-tuning at a glance
| RAG | Fine-tuning | |
|---|---|---|
| What it changes | What information the model sees | How the model behaves |
| Updating knowledge | Add or edit documents | Retrain with new data |
| Citations | Can show sources | Doesn't naturally cite |
| Best for | Questions about your content | Consistent formats, tone, specialised tasks |
| Data needed | Your documents | Many high-quality examples |
| Setup effort | Moderate | Higher |
When businesses want AI that “knows our stuff”, they often assume they need to train a model. In most cases, they don't. Retrieval is simpler, cheaper to update and easier to verify. Fine-tuning has its place, but for different problems. This comparison explains both in plain terms.
How RAG works
Your documents are split into passages and indexed, typically in a vector database. When a user asks a question, the system finds relevant passages and gives them to the model with the question. The model answers using that information.
Because knowledge lives in documents, updating it is as simple as updating the documents. Answers can link to sources, which helps users verify them.
How fine-tuning works
Fine-tuning trains a model on examples of inputs and desired outputs. The model learns patterns: a specific format, tone, classification scheme or task. It doesn't reliably memorise facts, and changing facts means retraining.
It's useful when prompts alone can't achieve consistent behaviour, or when a smaller fine-tuned model can replace a larger, more expensive one for a narrow task.
Which should you choose?
Choose RAG if…
You want answers from your documents, policies, products or data, with up-to-date information and sources.
Choose fine-tuning if…
You need a consistent output style or format, or a specialised task done cheaply at scale.
Combine if…
You need both domain behaviour and current knowledge.
Data and privacy
With RAG, documents stay in your storage and only relevant passages are sent with each request. Access controls can ensure users only retrieve what they're allowed to see.
Fine-tuning sends training data to the provider or requires your own training infrastructure. Review provider terms and data processing carefully in either case.
Before choosing, ask
- Is the goal knowledge or behaviour?
- How often does the information change?
- Do users need sources?
- Do we have many high-quality examples?
- What are the data privacy requirements?
Scenario: an internal policy assistant
A company wants staff to ask questions about HR policies, travel rules and IT procedures. Policies change regularly and answers must cite the current document. RAG is the clear choice: documents are indexed, answers reference sources, and updates are as simple as replacing a document.
Fine-tuning would struggle to keep up with changes and couldn't easily cite sources.
Scenario: consistent classification at scale
A company processes thousands of support tickets daily and wants them classified into specific categories with a consistent format. Fine-tuning a smaller model on labelled examples can produce consistent, cheap classification.
RAG adds little here, because the task is about behaviour, not knowledge.
Cost and effort comparison
| Aspect | RAG | Fine-tuning |
|---|---|---|
| Initial set-up | Index documents, build retrieval | Prepare training data, train, evaluate |
| Updating | Update documents | Retrain |
| Running cost | Retrieval plus model calls | Model calls, possibly cheaper with smaller models |
| Verification | Check sources | Test outputs |
Evaluating either approach
Build a test set of real questions or tasks with expected answers, and measure accuracy before launch and after every change. Include tricky cases: missing information, outdated facts, ambiguous questions.
Regular evaluation keeps quality stable as data, prompts and models change.
Prompting comes first
Before RAG or fine-tuning, good instructions often solve more than expected. Clear prompts with examples can shape tone and format without extra infrastructure.
Try prompting first, add RAG for knowledge, and consider fine-tuning only for specific remaining needs.
Maintenance
RAG systems need document updates, monitoring of retrieval quality and occasional re-indexing. Fine-tuned models need retraining when requirements or data change, and re-evaluation when base models are updated.
Assign an owner for whichever approach you choose.
Choose RAG when
- Knowledge changes often
- Answers need sources
- Data is spread across documents
- Permissions vary by user
Privacy and permissions in practice
RAG systems can respect existing permissions by filtering documents based on the user, so employees only see answers from documents they can access. Fine-tuned models can't easily enforce such per-user rules because knowledge is mixed into the model.
For sensitive internal data, this is a strong reason to prefer retrieval.
When neither is needed
For simple tasks (rewriting text, summarising a document the user provides, answering general questions) a well-prompted model may be enough without RAG or fine-tuning.
Start simple and add complexity only when tests show it's needed.
Sources and further reading
Frequently asked questions
Does RAG stop AI from making things up?
It reduces errors by grounding answers in your content, but doesn't eliminate them. Good retrieval, instructions and testing are essential.
Is fine-tuning expensive?
It requires quality training data, experimentation and evaluation, which adds cost compared with RAG.
Can RAG handle thousands of documents?
Yes, with good indexing, chunking and search strategies.
Do I need a vector database?
Most RAG systems use one, though some use traditional search or a combination.
Can RAG respect permissions?
Yes. Retrieval can filter documents based on what each user may access.
Can I combine RAG and fine-tuning?
Yes, for example a fine-tuned model for format and tone, with RAG for current knowledge.
Which is faster to build?
RAG, in most cases.
Do you build both?
Yes. We usually recommend RAG for knowledge use cases and fine-tuning only when there's a clear need.
Can RAG work with spreadsheets and databases?
Yes, with appropriate preparation, or by letting the AI query structured data through tools.
Do I need a large dataset for RAG?
No. RAG works with small or large document sets.
Is fine-tuning available for all models?
No. Availability depends on the provider and model.
Can RAG answer questions across many languages?
Yes, if the model supports them; documents can be in one language and questions in another.
Can RAG and fine-tuning use our own hosted models?
Yes, with open models hosted on your infrastructure, at the cost of more operational work.
Which is cheaper to run?
It depends on volume and models; RAG adds retrieval costs, fine-tuned smaller models can reduce per-call costs.
Can RAG systems show which document an answer came from?
Yes. Citations are one of RAG's main advantages.
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