Retrieval-Augmented Generation

RAG (Retrieval-Augmented Generation) is a way to help an AI model answer questions using information it can look up, rather than relying only on what it already knows.

Think of an employee asking an AI assistant, “What’s our current refund policy?” The model may understand the question, but it won’t necessarily know the latest version of the company’s policy. A RAG system finds the relevant document, passes the useful sections to the model, and lets it answer based on that information.

The process has three parts. First, the system retrieves information related to the question. Then it adds that information to the model’s input. Finally, the model generates an answer. A well-designed assistant can also show which documents it used, so the employee can check the answer for themselves.

RAG is useful when information changes often or lives outside the model. That might include internal procedures, product documentation, contracts, knowledge bases, or support articles. Instead of trying to fit all that information into a model, a company can keep its documents up to date and let the system search them when needed.

Finding the right information is often the hardest part. If a company has thousands of documents, the system needs to identify which passages actually answer the question. It may search by keywords, meaning, or both, then rank the results before sending them to the model. If it picks the wrong passage, even a capable model can produce an unhelpful answer.

RAG is different from fine-tuning. Fine-tuning changes a model through additional training. RAG leaves the model as it is and gives it relevant information at the time of the request. If an assistant needs access to the latest company policies, RAG may be a good fit. If it repeatedly struggles to perform a specialised task in the required way, fine-tuning may be worth testing. The two approaches can also be used together.

Businesses use RAG to help employees find answers in internal documents, support teams locate product guidance, and analysts work through large collections of reports. For example, instead of searching several folders for an onboarding procedure, an employee could ask a question and receive an answer linked to the relevant document.

It is not a guarantee of accuracy. A RAG system can retrieve an outdated document, miss an important exception, or generate an answer that goes further than the source supports. It also needs proper access controls: an assistant should not reveal a document to someone who would not normally be allowed to open it.

That is why teams need to test more than the model’s writing. They need to check whether the system finds the right sources, respects permissions, gives answers those sources support, and makes it clear when a person should step in.

In short, RAG helps an AI assistant work with information it can look up when a question is asked. Its usefulness depends on the documents behind it, the quality of the search, and how carefully the answers are checked.