In the rapidly evolving landscape of generative artificial intelligence (Gen AI), large language models (LLMs) such as OpenAI’s GPT-4, Google’s Gemma, Meta’s LLaMA 3.1, Mistral.AI, Falcon, and other AI tools are becoming indispensable business assets. One of the most promising advancements in this domain is Retrieval Augmented Generation (RAG). But what exactly is RAG, and how can it be integrated with your business documents and knowledge? RAG is an approach that combines Gen AI LLMs with information retrieval techniques. Essentially, RAG allows LLMs to access external knowledge stored in databases, documents, and other information repositories, enhancing their ability to generate accurate and contextually relevant responses. As Maxime Vermeir, senior director of AI strategy at ABBYY, a leading company in document processing and AI solutions, explained: “RAG enables you to combine your vector store with the LLM itself. This combination allows the LLM to reason not just on its own pre-existing knowledge but also on the actual knowledge you provide through specific prompts. This process results in more accurate and contextually relevant answers.”  This capability is especially crucial for businesses that need to extract and utilize specific knowledge from vast, unstructured data sources, such as PDFs, Word documents, and other file formats. As Vermeir details in his blog, RAG empowers organizations to harness the full potential of their data, providing a more efficient and accurate way to interact with AI-driven solutions.

Full report : How to integrate generative AI LLMs with your business knowledge.