Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response.

Feb 10, 2025 · Retrieval-augmented generation (RAG) is an innovative approach in the field of natural language processing (NLP) that combines the strengths of retrieval-based and generation-based models to enhance the quality of generated text. …

Dec 5, 2023 · RAG is a framework for improving model performance by augmenting prompts with relevant data outside the foundational model, grounding LLM responses on real, trustworthy information. Users can...

Jul 2, 2023 · This article is designed to demystify RAG, breaking it down into simple, easy-to-understand terms. We’ll explore what RAG is, how it works, its advantages, and why it’s a game-changer in the ...

Oct 22, 2024 · The key idea behind RAG is to synthesize the accuracy and search capabilities of information retrieval techniques typically used by search engines, with the in-depth language understanding and generation capabilities of LLMs.

Oct 22, 2024 · While many enhanced and more sophisticated versions of RAG keep spawning almost daily as part of the frantic AI progress nowadays, the first step to understanding the latest state-of-the-art RAG approaches is to first comprehend the classic RAG workflow.

Mar 20, 2025 · In this new installment of our Understanding RAG article series, we will examine the problem of hallucinations, how they manifest in RAG systems compared to standalone language models, and most importantly, how to navigate this challenging issue.

Jan 5, 2024 · Retrieval Augmented Generation (RAG) represents a breakthrough in natural language processing, bridging the gap between retrieval-based and generative models. It has revolutionized how we...

Retrieval augmented generation, or RAG, is an architectural approach that can improve the efficacy of large language model (LLM) applications by leveraging custom data. This is done by retrieving data/documents relevant to a question or task …

Mar 17, 2025 · Retrieval-augmented generation (RAG) is an innovative AI framework that synergizes information retrieval with generative models. By using external data sources to inform responses, RAG significantly enhances the quality and relevance of output generated by LLMs.