Think of Retrieval-Augmented Generation (RAG) as a clever assistant who, before answering your question, first flicks rapidly through the relevant encyclopaedia or database. Instead of relying only on his learned memory, he actively looks up current, specific information and only then formulates an answer on that basis. This makes his answers more accurate, factually grounded and less prone to making things up.
Retrieval-Augmented Generation (RAG) is a hybrid architecture in natural language processing that combines the capabilities of pre-trained language models with an external retrieval mechanism. The model does not generate an answer solely from its internal, parametrically stored knowledge; it first searches an extensive corpus of documents (a database or a set of web pages, for instance) and retrieves relevant passages of text. It then uses this retrieved information as the context from which it generates the final answer.
The process consists of two main phases: retrieval and generation. In the retrieval phase the user query is converted into a numerical representation (a vector) and compared with previously indexed documents in order to find the most similar ones. In the generation phase a language model, typically of the transformer type, receives the original query together with the content of the documents found and on that basis formulates coherent, contextually relevant text. RAG effectively reduces the risk of hallucinations and allows the model’s knowledge to be updated easily by simply swapping the external data source.