The magic of RAG is in the retrieval [Video]

Any leading large language model will do. To succeed with retrieval-augmented generation, focus on optimizing the retrieval model and ensuring high-quality data.

Credit: Annette Shaff / Shutterstock

The decades-long pursuit to capture, organize and apply the collective knowledge within an enterprise has failed time and again because available software tools were incapable of understanding the noisy unstructured data that comprises the vast majority of the enterprise knowledge base. Until now. Large language models (LLMs) that power generative AI tools excel at processing and understanding unstructured data, making them ideal for powering enterprise knowledge management systems.

To make this shift to generative AI work in the enterprise, a dominant architectural pattern has emerged: retrieval-augmented generation (RAG), combined with an “AI agents” approach. RAG introduces an information retrieval component to generative AI, allowing systems to access external data beyond an LLM’s training setand constrain outputs to this specific information. And by deploying a sequence of AI agents to perform specific tasks, teams …