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The Augmented Generation by Retrieval (AGR) is a recent method in artificial intelligence that combines information retrieval and content creation.

Usually, large-scale language models (LLMs) produce content based solely on the information acquired during their training. However, AGR allows the model to "consult" a database or a set of external documents in real time to enrich the text it generates.

The AGR process is divided into two main steps:

1. Retrieval: when the model receives a request, it searches a predefined set of documents or data to find the most relevant information related to the request.

2. Generation: after retrieving the relevant information, the model uses it, in addition to its own internal knowledge, to generate a response or content that not only meets the initial request, but does so in a more informed and precise manner.

Integrating AGR into generative AI systems offers numerous significant advantages, improving not only the quality of the generated content, but also its applicability in various contexts. By relying on an external database to complement its knowledge, an AGR model can provide more precise and relevant answers to questions posed.