RAGs to Riches: Elevating AI with External Data
LLMs such as GPT have certain limitations. They may not have up-to-date information due to their knowledge cutoff date for training. This poses a significant challenge when we want our AI models to provide accurate, context-aware, and timely responses. Imagine asking an LLM a question about the latest technology trends or seeking real-time updates on a breaking news event; traditional language models might fall short in these scenarios.
In this chapter, we’re going to introduce you to a game-changing technique called retrieval-augmented generation (RAG), an outcome of the work carried out by researchers at Facebook AI (now Meta). It’s the secret sauce that empowers language models such as GPT to bridge the gap between their static knowledge and the dynamic real world. With RAG, we’ll show you how to equip your generative AI applications with the ability to pull in fresh information, ground your organizational data...
