RAG vs Fine-Tuning: Which Should You Use for Your AI Application?

When teams start building with large language models, one question comes up almost immediately: should we use RAG or fine-tune a model? The two approaches are often presented as alternatives, but they solve different problems. Choosing well can save months of work.

The short answer

• Use RAG when the model needs to know things — your documents, products, policies or data — especially when that information changes.
• Use fine-tuning when the model needs to behave differently — follow a specific format, tone or task pattern consistently.

Many production systems use both.

What is RAG?

Retrieval-Augmented Generation adds a search step before the model answers:

1. Your content is split into chunks and indexed — typically as embeddings in a vector database, often combined with keyword search.
2. When a user asks a question, the most relevant chunks are retrieved.
3. Those chunks are passed to the model as context, and the model answers using them.

Because knowledge lives in the index rather than the model, updating it is as simple as re-indexing a document.

Strengths

• Answers can be grounded in, and cite, your own sources.
• Knowledge stays fresh without retraining.
• Access control can be enforced at retrieval time, so users only see what they're allowed to.

Watch out for

• Retrieval quality is everything — poor chunking or search leads to poor answers.
• Longer prompts increase latency and cost per request.

What is fine-tuning?

Fine-tuning continues training a model on examples of the inputs and outputs you want. The model's weights change, so the behaviour is "baked in".

Strengths

• Consistent output formats, styles and classifications.
• Shorter prompts, because instructions don't need repeating every time.
• Can make a smaller, cheaper model perform well on a narrow task.

Watch out for

• It's a poor way to teach facts — knowledge is hard to update and hard to verify.
• You need a good, representative training dataset.
• Every change means another training run and another round of evaluation.

A side-by-side comparison

	RAG	Fine-tuning
Best for	Knowledge and facts	Behaviour and format
Updating information	Re-index documents	Retrain the model
Source citations	Natural	Not built in
Upfront effort	Indexing pipeline	Curated training data
Per-request cost	Higher (longer prompts)	Lower (shorter prompts)

When to combine them

A common pattern is a support assistant that uses RAG to pull the right help-centre articles and a fine-tuned model (or careful prompting) to answer in the company's tone and structure. Retrieval provides the facts; tuning provides the consistency.

Our recommendation

Start with a strong base model, good prompts and RAG. Measure quality with a realistic evaluation set. Only reach for fine-tuning when you've identified a behaviour problem that prompting can't fix — or when you need to reduce cost at scale.

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