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---
title: "RAG or fine-tuning? How to choose for your company's documents"
description: "RAG and fine-tuning solve different problems. A practical guide to understanding which approach your company actually needs to get AI to reason over its documents."
date: 2026-06-24
tags: [RAG, AI architecture]
url: "https://ai.malagoli.me/en/blog/rag-vs-fine-tuning-guida-pratica"
locale: en_US
---

# RAG or fine-tuning? How to choose for your company's documents

RAG and fine-tuning are often presented as equivalent alternatives. They aren't: they solve different problems, and choosing the wrong one costs months of work.

When a company wants AI to "know" its own documents — contracts, internal procedures, technical catalogs — the question that almost always comes up is whether it's worth fine-tuning the model. In most cases the answer is no, and the reason lies in what these two techniques actually solve.

## What RAG does

RAG (Retrieval-Augmented Generation) retrieves the relevant passages from your documents at the moment of the query and feeds them to the model as context for its answer. The model doesn't "learn" your documents: it reads them every time, in real time, from an index you update simply by adding or editing files.

## What fine-tuning does

Fine-tuning changes the model's weights by training it on specific examples, to change its style, response format, or underlying behavior. It's not a good mechanism for injecting factual knowledge that changes over time: if a contract is updated, a fine-tuned model doesn't know it until you retrain it.

## The framework we use with clients

- Does the content change often (procedures, catalogs, regulations)? RAG is almost always the right choice.

- Do you need a specific tone, format, or behavior that stays stable over time? Fine-tuning makes sense, often combined with RAG.

- Do you need traceability — knowing which document a given answer came from? Only RAG offers this naturally, by citing the retrieved sources.

- Is the volume of knowledge enormous and highly specialized? A hybrid approach is worth considering, with RAG for facts and a base model chosen for the domain.

## Why it pays to start with RAG

RAG costs less to maintain, is simpler to explain in an audit — because you can show exactly which document generated a given answer — and fits an open-weight infrastructure hosted internally without needing to retrain anything every time a document changes. For most of the business use cases we encounter, it's the strongest starting point, with fine-tuning reserved for the cases where it's genuinely needed.

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