Retrieval-augmented generation concept

RAG versus fine-tuning

Reference

RAG vs fine-tuning: RAG retrieves relevant material and gives it to a model when it answers; fine-tuning trains a model further on examples to change how it responds. For company data that changes or needs source references, RAG is often the more direct fit. For a consistent style, format, or task behavior, fine-tuning may help. They can also be combined.

Reviewed 2026-10-09

Two ways to adapt a model

RAG means retrieval-augmented generation: an application searches a collection for material relevant to a question, then includes selected material in the model’s input. The model uses that context to generate a response. Fine-tuning means further training a pretrained model on task-specific examples, adjusting its parameters to better match those examples. In short, RAG supplies information for a particular answer; fine-tuning changes the model through training. They address different needs, though an application can use both.

What changes, and when

With RAG, the collection is searched as part of answering a question. A team can update or replace material in that collection without making those documents into training examples. The system needs to prepare searchable data and retrieve relevant passages. With fine-tuning, examples are used in a training process to adjust the model’s behavior; this can help with a recurring task, terminology, or output style. A fine-tuned model does not automatically look up new company facts for each answer. RAG adds retrieval work, and fine-tuning requires suitable examples and evaluation.

Choosing for your use case

For company policies, product details, or internal guidance that may change, start by considering RAG: it can retrieve from connected documents and include the selected material in the request. This is especially useful when readers need answers tied to source material, provided the system retrieves relevant passages and access rules are applied. Consider fine-tuning when the main goal is to make responses follow a repeatable style, format, or task pattern, and you can provide representative input-and-output examples. Test the approach against real questions and the errors that matter for your use.

Common confusions

Fine-tuning a model on company documents is not the same as giving it a searchable, current company library: fine-tuning uses training examples, while RAG retrieves material for a response. RAG can return citations, but adding retrieval does not guarantee the answer is correct or that a citation supports every claim. Check distinct failure points: retrieval quality, whether the answer follows the retrieved material, factual correctness, citation support, and permissions. Poor retrieval can lead to incomplete answers, and weak access controls can expose sensitive content. A combined setup can retrieve company facts while using a fine-tuned model for response style or task behavior.

Questions people ask

Can RAG use private company documents?

Yes. A RAG application can retrieve material from connected private documents and provide it to the model. The system needs access controls at retrieval time so users only receive material they are allowed to see.

Does fine-tuning add the latest company facts to a model?

Not automatically. Fine-tuning trains on examples to adjust model behavior; it does not make the model query a live company collection for every answer. Use retrieval when answers need changing source material.

Can a team use RAG and fine-tuning together?

Yes. RAG can provide relevant source material for an answer, while fine-tuning can help shape a recurring task or response style. Evaluate the combined system for retrieval quality, answer faithfulness, factual correctness, citation support, and permissions.

Does RAG guarantee accurate answers or citations?

No. Irrelevant or incomplete retrieved material can still lead to an inaccurate answer, and a model can be inaccurate even when given retrieved content. Check whether each citation actually supports the answer.

Learn more

Retrieval augmented generation (RAG) and indexes
Explains RAG’s retrieve, augment, and generate flow, with guidance on access control and common failure points.
Fine-tuning LLMs and AI models
Describes fine-tuning as further training on task-specific data and outlines training examples, evaluation, and tradeoffs.
To tune or not to tune? A guide to leveraging your data with LLMs
Compares retrieval and supervised fine-tuning, including examples of how the approaches can be combined.

What changed

Sources

Part of Retrieval-augmented generation: The retrieval and answer pipeline Find and test failure points