Let's make chatbots smarter using REAPER

Author(s): Manpreet Singh

Originally published on Towards AI.

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Picture this: you’re online, chatting with a shopping assistant about your order, or you’re curious if a phone you’re eyeing is compatible with your new earbuds. The chatbot, quick as lightning, responds with precise information — almost as if it knows you personally. What you don’t see is the fascinating technology making it all happen.

Credit: https://unsplash.com/illustrations/a-man-running-with-a-book-and-a-cell-phone-35Sqe_mCcGw

One fine day, while researching, I found a paper mentioning a term REAPER. Then I find out it's a brand-new approach to handling these complex tasks that transform how chatbots work behind the scenes. Today, we’re going to break down REAPER and see why it’s a game-changer for conversational systems.

First lets understand what Are RAG Systems, and Why Do They Matter?

Let’s start with something called RAG, which stands for Retrieval Augmented Generation. It’s a fancy term, but it boils down to this: chatbots use massive amounts of data to respond accurately to your questions. Instead of just making things up from what they know, they go out and fetch specific facts — kind of like a librarian hunting down the exact book or article you need. This ensures the answers you get are not… Read the full blog for free on Medium.

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Published via Towards AI