Retrieval-Augmented Generation (RAG): LLMs with Real-Time Knowledge

Author(s): Shivam Mohan

Originally published on Towards AI.

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Artificial Intelligence (AI) constantly evolves, and Retrieval-Augmented Generation (RAG) is at the forefront of this revolution. By merging the text generation capabilities of language models with real-time information retrieval, RAG is reshaping how AI, especially Generative AI systems process, retrieve, and generate responses. This article explores the technical workings of RAG, including how data is ingested into a vector database, how relevant information is retrieved, and how AI generates responses based on both pre-trained knowledge and retrieved data.

Retrieval-Augmented Generation (RAG) is a hybrid AI model that combines the generative capabilities of models like GPT with a retrieval system that fetches real-time, up-to-date information from external sources (e.g., databases or the internet). Unlike static language models, which can only generate responses based on the data they were trained on, RAG systems can retrieve the latest information to ensure their responses are accurate, current, and relevant.

RAG’s development was pioneered by researchers from Facebook AI Research (FAIR), including Patrick Lewis and Ethan Perez. Their work addresses a key limitation in traditional language models: the inability to access information beyond their training data. By integrating transformer-based models with retrieval systems, the FAIR team… Read the full blog for free on Medium.

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Published via Towards AI