RAG 2.0, Finally Getting RAG Right!

Author(s): Ignacio de Gregorio

Originally published on Towards AI.

The Creators of RAG Present its Successor

Looking at the AI industry, we have grown accustomed to seeing stuff get ‘killed’ every single day. I myself cringe sometimes when I have to talk about the 23923th time something gets ‘killed’ out of the blue.

But rarely the case is as compelling as what Contextual.ai has proposed with Contextual Language Models (CLMs), in what they call “RAG 2.0”, to make standard Retrieval Augmented Generation (RAG), one of the most popular ways (if not the most) of implementing Generative AI models, obsolete.

Behind the claim, none other than the initial creators of RAG.

And while this is a huge improvement over the status quo of production-grade Generative AI, one question lingers over this entire subspace: is RAG counting its last days, and are these innovations simply beating a dead horse?

As you may know or not know, all standalone Large Language Models (LLMs), with prominent examples like ChatGPT, have a knowledge cutoff.

What this means is that pre-training is a one-off exercise (unlike continual learning methods). In other words, LLMs have ‘seen’ data until a certain point in time.

For instance, ChatGPT is updated until April 2023 at the time of writing. Consequently, they are not prepared to answer about facts and events that took… Read the full blog for free on Medium.

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Published via Towards AI