Advanced RAG Techniques: Bridging Text and Visuals for More Accurate Responses

Retrieval-Augmented Generation (RAG) is a technique that combines large language models' generative abilities with retrieval systems to fetch relevant information from external sources, improving the accuracy and contextual relevance of AI responses. Advanced RAG techniques like Small to Slide enhance performance when dealing with visual data such as presentations or documents with images. RAG methods require infrastructure to manage complex queries and retrieval operations, and emerging techniques like ColPali work directly with visual features of documents, enabling it to index and retrieve information without the error-prone step of text extraction.