Comparison
RAG vs. fine-tuning — what should you build?
RAG is often the right first step when answers must come from your documents. Fine-tuning wins on format, style, and narrow classification. Here is the honest difference.
The decision
Most ‘AI on our data’ projects should start with retrieval.
RAG (retrieval-augmented generation) fetches relevant chunks from your corpus and asks the model to answer from them. It is strong when content changes and you need citations.
Fine-tuning changes model behaviour. It is strong when you want a specific output format, tone, or classification — not when you mainly want to ‘search the PDFs’.
We build RAG systems when the use case is document Q&A with evaluation. We say no to fine-tuning as the first answer to a retrieval problem.
What usually decides it
Practical signals from discovery.
If you recognise the left column in the comparison below, start with RAG.
Documents that change
RAG — indexing and chunking you can update.
Requirement for citations
RAG — show the source in the UI.
Fixed output schema
Fine-tuning or strong prompting/structured output.
High-volume classification
Fine-tuning can be cheaper to run than huge prompts.
Agents that must act
Often RAG + tools — not only fine-tune.
No eval set
Build it before you scale. Otherwise you are guessing.
RAG vs. fine-tuning
RAG
Fetch knowledge at query time from your corpus.
- Good for document Q&A that changes
- Citations and ‘I found nothing’ are natural
- Updated via an indexing pipeline
- Needs chunking, metadata, and eval
- Permissions must be built in
- Hallucinations are controlled — not magically removed
Fine-tuning
Adapt model behaviour with training data.
- Good for format, style, and classification
- Harder to update when facts change
- Needs high-quality training data
- More expensive and heavier to iterate wrong
- Does not solve document ACL alone
- Often a layer on top of solid retrieval
RAG or fine-tune?
More left → RAG first. More right → consider fine-tuning (possibly after RAG).
RAG when…
Answers must come from specific documents
Fine-tune when…
You need a very specific style or schema
RAG when…
Content changes often
Fine-tune when…
Facts are stable and behaviour is the problem
RAG when…
You need source citations
Fine-tune when…
You classify or extract at high volume
RAG when…
You can build indexing + eval
Fine-tune when…
You have a clean training set and a narrow goal
Watch out
Classic mistakes.
Fine-tune to ‘remember’ documents
Wrong tool. Retrieval is built for that.
RAG without permissions
If documents have ACLs, retrieval must respect them.
No evaluation
Without a Q&A set you cannot tell if you are getting better.
ChatGPT on files as production
Fine as a spike. Poor as a system for sensitive data without control.
FAQ
RAG vs. fine-tuning
Can we combine both?
Yes. Many production systems use RAG for facts and fine-tune/prompting for format. Start with the part that hurts most.
Which vector database?
The one you can operate. Postgres + pgvector is a strong default if you already run Postgres.
Are agents the same as RAG?
No. Agents use tools and planning. RAG is a retrieval strategy agents often need.
How do we start without over-investing?
A bounded corpus, an eval set, and a UI with citations. Then you know whether it is worth building further.
Read next
Read next
On RAG and AI in production.
- AI6 min read
When does RAG make sense — and when is it the wrong AI project?
RAG has become the default answer to "we want to use our own data with AI". It's often right — and expensively wrong when the real problem is search, process, or data quality. Here's the honest sorting.
→When does RAG make sense — and when is it the wrong AI project? - AI3 min read
AI agents in production — what must be in place before you ship
A demo agent that answers smartly in ChatGPT is not a production agent. Shipping AI to real users requires boundaries, observability, and a clear definition of when a human takes over.
→AI agents in production — what must be in place before you ship
AI on your data?
Let’s sort RAG from fine-tune — before you build the wrong thing.
We help with use case, corpus, and eval so you get a system that can go to production.