It’s been fewer than three years since the first release of ChatGPT. The initial large language models (LLMs) were popular, but far from accurate. So retrieval-augmented generation (RAG) emerged as an approach that markedly improves generative AI results by automatically feeding current and relevant proprietary data into LLMs.
RAG incorporates structured data from spreadsheets and relational databases as well as unstructured data from emails, PDFs, chats, social media and more. It preprocesses and indexes this information and harnesses…
