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Use retrieval-augmented generation (RAG) when an AI needs relevant private or up-to-date information; consider fine-tuning when it has the information but consistently handles a task, format, or style poorly. RAG supplies material to the model for a particular request. Fine-tuning adapts the model using training examples. They solve different problems, and a system can use both if evaluation shows it needs both.
What’s the difference between RAG and fine-tuning?
RAG and fine-tuning affect different parts of an AI application. RAG changes the information available to the model for a response; fine-tuning changes the model by adapting it with examples. OpenAI describes RAG as retrieving content to augment a model’s prompt before generating an answer. OpenAI’s optimization guide and Microsoft’s RAG guide explain these as distinct approaches.
RAG retrieves information for the request
In a RAG workflow, the application searches an external index or data store for relevant material, then includes selected passages with the user’s question in the model’s prompt. The model generates an answer using that supplied context. The underlying model is not retrained on those documents for that request.
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The search may use keyword matching, semantic search, vector similarity, or a combination. Vector search uses numerical representations called embeddings to find content with related meaning. OpenAI describes vector stores and semantic search in its Retrieval guide; Microsoft outlines index options in its Foundry RAG guide.
Fine-tuning adapts model behavior with examples
Fine-tuning uses a prepared training set to adapt an existing model. It can help with a scoped task, a particular skill, or more consistent output behavior, such as a required format or style. Microsoft describes supervised fine-tuning as iterative adaptation using a provided training set in its model customization guide.
Fine-tuning is not a live database that automatically reflects changing policies or facts. Its effectiveness depends on the quality and relevance of the examples, and the adapted model still needs evaluation.
Why does AI keep recommending RAG?
RAG is an accessible first option when the problem is missing information: perhaps answers need to reflect internal documents, current policies, or other material that was not in the model’s training data or has since changed. A team can update the content and retrieval index separately from the model’s training. Retrieved passages can also be exposed as sources in an application, making it easier to inspect what information supported an answer.
That convenience does not make RAG automatically accurate. The search step must find relevant material, and the generation step must use it appropriately. Irrelevant, incomplete, or outdated passages can still lead to a poor answer. Microsoft discusses the retrieval and generation components in its RAG guide.
Should you use RAG or fine-tune your model?
Start by identifying what is going wrong: is the model missing the right information, or does it behave inconsistently even when it has that information? The first points toward improving context, potentially with RAG. The second points toward improving instructions or model behavior, potentially with fine-tuning. OpenAI recommends evaluating actual failures and choosing the optimization lever that addresses them, rather than treating prompt engineering, RAG, and fine-tuning as a fixed ladder. OpenAI’s optimization guide describes that evaluation-led approach.
| Decision point | RAG | Fine-tuning |
|---|---|---|
| Main target | Supply relevant external context for a response | Teach or stabilize model behavior for a task |
| Private or changing facts | Often a strong fit when the source or index can be updated | Usually a poor sole mechanism for frequently changing facts |
| Format, style, or task consistency | Can supply examples or instructions, but does not itself change learned behavior | Can be a fit when representative, high-quality examples support it |
| Typical preparation | Prepare and index content; make retrieval relevant | Prepare examples, fine-tune, and evaluate the adapted model |
| Evidence available for an answer | Retrieved passages may be shown or inspected | Knowledge or behavior is reflected in the adapted model; evidence is not automatically retrieved for each answer |
These are general decision guides, not guarantees of improvement for a particular application. Establish a baseline, evaluate representative failure cases, apply the change that targets them, and reassess the results.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When should you use both?
Use both only when evaluation identifies two separate needs: the model needs external facts at answer time and it also needs more reliable behavior on the task. For example, a system might retrieve current internal policy passages while relying on a fine-tuned model to consistently return answers in a required structure. RAG addresses the supplied information; fine-tuning addresses learned behavior. Combining them adds implementation and evaluation work, so neither technique should be added just because it is available.
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