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RAG vs fine-tuning

We want it to know our documents. Which one do we need?

The short answer

Almost always retrieval, not fine-tuning. RAG looks your documents up at the moment of the question and can cite them, and it updates the second a document changes. Fine-tuning teaches style and format, not facts, and a fine-tuned model still cannot tell you where an answer came from.

Retrieval-augmented generation

Search, Embeddings and RAG · Intermediate

In one sentence: A method that first finds relevant source passages and then asks a language model to answer using that material.
Why it matters: For non-specialists, this method matters because it can make AI answers more checkable. Instead of relying only on patterns learned during training, the system can pull current source material at answer time. That can improve usefulness for policies, manuals, technical documents and large knowledge collections that change often. It also changes what you should ask before trusting a system. You should ask: What documents can it access? How recent are they? Can I inspect the cited passages? What happens when nothing relevant is found? A polished answer without good retrieval can still be wrong. Good governance therefore depends not only on the model, but also on document quality, indexing, access controls and human oversight.
The everyday version: It is like giving the AI an open folder of relevant pages before asking it to answer, so the reply can be based on those pages instead of memory alone.
The one thing to remember: RAG retrieves source passages and places them in the model’s prompt before it answers. This can improve relevance and traceability, but retrieved material may still be missing, outdated or misranked.
The commonest misconception: RAG guarantees factual answers because it uses sources. It can improve traceability, but it does not guarantee accuracy. The system may retrieve the wrong passage, miss an important document, use outdated material or generate wording that goes beyond the source. Citations help checking; they do not replace checking.

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Fine-tuning

Training and Model Adaptation · Starter

In one sentence: Updating a pre-trained model's parameters with additional examples to improve performance on a narrower task, style or domain.
Why it matters: Fine-tuning matters because organisations often need more than a general model. They may want a model that follows a specific format, works better in a specialist domain, or uses approved terminology more consistently. For leaders and policy staff, this affects cost, risk, procurement and governance: what data can be used, who checks quality, how a new model version is evaluated, and whether changing the model is even necessary. In many cases, a prompt or document retrieval system is enough. In other cases, fine-tuning can improve reliability on a narrow task. Knowing the difference helps you ask better questions before spending money or exposing data.
The everyday version: Fine-tuning means taking a general AI model and giving it extra practice on a narrower job so it performs that job more consistently.
The one thing to remember: Fine-tuning updates a pre-trained model's parameters using additional examples and produces a new version for a narrower job. It is not prompting, and it still requires authorised data, evaluation and human review.
The commonest misconception: Fine-tuning simply means the model has read our documents. Not exactly. Fine-tuning is a training process that changes model parameters using curated examples. Giving documents at use time is different; that is closer to retrieval-augmented generation or context prompting, where the model itself may remain unchanged.

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