What is RAG?

Retrieval-Augmented Generation (RAG) is an advanced approach in artificial intelligence that seeks to boost the performance of large-scale language models (LLMs) . It achieves this by integrating their generative capabilities with external mechanisms that retrieve relevant information.

Unlike traditional models , which rely solely on the data they were trained on, this approach allows access to updated knowledge sources in real time . This solves one of the main challenges of LLMs: outdatedness, missing data, and (to some extent) model hallucinations.

In business contexts, this technology is especially useful, as it allows you to leverage information stored on platforms such as Confluence , Jira , Google Drive, SharePoint to name a few.

By combining the model's reasoning with data obtained from external databases —such as documents, web pages, or specialized databases— RAG allows for the generation of more precise, relevant, and tailored responses to the query's specific environment .