An AI can answer a question without knowing the document that contains the correct answer. The answer given as if from a native speaker might lack essential elements from a source that it does not have access to. School handbooks, companies' internal regulations, and recently updated documentation in general tend to have elements which the AI does not include in its answers. These issues are solved by the retrieval augmented generation's ability to retrieve relevant documents.

What Is RAG?

Retrieval augmented generation, also known as RAG, combines information retrieval and text generation. According to IBM, the system works by having a system search through a knowledge base and retrieve relevant information which it then uses for generating an answer.

The extraneous information is used as context for the model to provide a better answer than it otherwise would have given.

Why Finding the Correct Data Source Matters

Suppose the student asks the AI assistant about the school's absence policy. The general answer might sound plausible, but it does not take into consideration the handbook that the student is using as a reference. The assistant hypothetically could retrieve the relevant part of the handbook and use it as preparation for the answer.

It is clear that, in order to give the correct answer, the AI would have to have access to the necessary data source. If the question requires a specific answer, the general knowledge that the model has will not suffice. Similarly, the system must be able to retrieve the correct passage; the relevance of the information is crucial.

The Value and Limits of Contextual Answers

IBM mentions that RAG's ability to cite relevant sources reduces the possibility of presenting fabricated information. However, it is not completely incapable of providing false answers. IBM adds that answers generated by RAG have references, which means that anyone can check the validity of the claims that the AI makes.

A citation after the generated text is not a sign of an impeccable answer. One must analyse the given source to determine if the information is relevant, recent enough, and completely accurate.

Conclusion

The RAG method appends the information retrieval process to the procedure of generating an answer for an AI model. The method is useful because it provides the ability to answer questions using documents that the model would not have been able to use before. As is always the case with research, the quality of sources is what is ultimately important. This fact is doubly true for RAG: the answer is only as good as the retrieval of information that it presents as facts.

Source

IBM. "What Is Retrieval-Augmented Generation?" IBM , 2026.