What is Retrieval-Augmented Generation (RAG)?

Retrieval-augmented generation (RAG) is an advanced natural language processing approach that combines retrieval and generation techniques to produce more accurate and contextually relevant text. In RAG, a retrieval system first searches a large corpus of documents to find relevant information based on a given query. Then, a generative model uses this retrieved information to construct a coherent and contextually appropriate response. This method enhances the quality of generated text by grounding it in actual data, making it particularly useful for tasks requiring detailed and precise information.

Join us to learn how to protect your unstructured data at rest, in transit, and in use in today’s AI-powered, hybrid workd environment.

Keep me informed
Privacy Overview
Fasoo

This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful.

3rd Party Cookies (Analytics)

This website uses Google Analytics to collect anonymous information such as the number of visitors to the site, and the most popular pages.

Keeping this cookie enabled helps us to improve our website.