In a recent blog post we gave an overview of Retrieval Augmented Generation (RAG) as a way to leverage the natural language querying capabilities of Large Language Models (LLMs) on private or proprietary stores of unstructured data. A major use case for RAG is in the creation of chatbots which

Retrieval augmented generation, or RAG, is rapidly gaining traction as the next disruptive evolution for generative AI models. Promising a reduction in the hallucinations sometimes experienced by large language models such as GPT-4, greater accuracy of responses, access to up-to-date information, and the capability to use private or proprietary data