Retrieval-Augmented Generation
A plain-language guide to Retrieval-Augmented Generation (RAG): what it is, its key components, and how it works.
Read the blog →What is Retrieval-Augmented Generation?
Retrieval-Augmented Generation (RAG) is a method that combines the strengths of information retrieval and text generation. It enhances the capabilities of large language models (LLMs) by dynamically retrieving relevant information from external sources and using this information to generate accurate and contextually relevant responses. This hybrid approach makes RAG particularly effective for tasks that require up-to-date and specific information.
In the world of Artificial Intelligence (AI) and Natural Language Processing (NLP), innovations are constantly emerging to improve how machines understand and generate human language. One of the most exciting advancements in this field is Retrieval-Augmented Generation (RAG). This guide will take you through the basics of RAG, its components, and how it works.
But what exactly is RAG, and how does it work? Let’s dive in and explore!
Latest posts
-
Answering in the reader's language
The question is in French and the corpus is in English. What that does to quotes, citations, defined terms and your faithfulness checks.
-
When the question contains a false premise
Asked why a policy allows fifteen days when it allows ten, a grounded model will explain the fifteen. How to make it check the premise first.
-
The model does not know what day it is
Documents talk about now and next quarter. The model has no clock. How to make time resolvable in a grounded answer instead of guessed.
-
When the answer is a procedure
A how-to answer someone will execute fails differently from a factual one. Dropped steps, merged variants, and lost conditionals.
-
Arithmetic the sources didn't do
A total the model computed appears in no document. Derived figures are new claims, they can't be cited, and they are wrong more often than they look.