New RAG Techniques are Generative AI’s Greatest Hope

As time goes on and some buyers of generative AI grow disillusioned with the meager returns they’ve seen, many are experimenting with new ways to eke better performance out of large language models without paying up for the latest, state-of-the-art LLM.

One way to do this is through a technique called retrieval augmented generation, which aims to reduce the AI’s tendency to hallucinate or give incorrect answers by giving it access to real-time data from the internet or a company’s own documents. This approach started picking up steam last summer, but like most things in AI, it’s evolved quickly, and already, developers are experimenting with new ways to improve RAG.