In the world of natural language processing (NLP), the RAG (Retriever-Reader with Answer Generation) model was once considered a groundbreaking approach to question answering. However, with Google’s recent announcement of a 1 million+ context window, the RAG model has lost much of its relevance.

Google just dropped a bombshell with their recent announcement. They’re rolling out a 1 million+ context window and it’s a game-changer.

Okay, let’s backtrack a bit. The RAG model works in two steps. First, it fetches relevant documents related to the question (that’s the retriever part). Then, it scans through those documents to find the answer (the reader part). Pretty cool, huh?

But here’s the kicker. The RAG model had a fixed context window, meaning it could only consider a limited amount of text at a time. So, for really complex questions needing a broader context, it sometimes fell short.

Now, Google’s new move changes everything. With this 1 million+ context window, models can analyze a ton more text. Think multiple documents, various sources—the whole shebang. This means more accurate and comprehensive answers.

So, where does that leave the RAG model? Well, it’s kind of taken a backseat now. While it was once cutting-edge, its fixed context window limitation makes it less relevant compared to Google’s shiny new approach.

But hey, don’t count it out completely. The RAG model still has its uses, especially in certain situations. But in the fast-paced world of NLP, it’s all about pushing boundaries and adapting to the latest innovations.