5 Smart Ways to Use Retrieval-Augmented Generation (RaG) for Real-Time NLP Enhancements

Author(s): Mukundan Sankar

Originally published on Towards AI.

How Retrieval-Augmented Generation (RAG) Can Boost NLP Projects with Real-Time Data for Smarter AI Models

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Image illustrating how Retrieval-Augmented Generation (RAG) can boost NLP projects with real-time data for smarter AI models generated using ChatGPT by the Author

We’ve seen some pretty amazing advancements in Natural Language Processing (NLP) recently. With models like GPT-3 and BERT, it feels like we’re able to do things that were once just sci-fi dreams, like answering complex questions and generating all kinds of content automatically. But, as great as these models are, they do have one major flaw — they’re stuck with the data they were trained on. So, if something has changed since then, or if new information is available, these models won’t know about it. That’s a big deal in today’s fast-moving world.

Enter Retrieval-Augmented Generation (RAG) — the tool that can help us bridge that gap. It’s like giving your model a magic wand that lets it pull in the latest information from external sources, right before generating a response. This way, your model stays current and relevant, no matter how quickly things change.

In this post, I want to walk you through five cool ways you can use RAG to seriously boost your NLP projects. Whether you’re… Read the full blog for free on Medium.

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Published via Towards AI