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Retrieval-Augmented Generation (RAG) is a trending concept in the field of Generative AI and Large Language Models (LLMs). It involves using LLMs to generate responses and retrieve information from a knowledge base for more accurate answers. Various companies and experts are exploring and utilizing RAG to enhance AI applications, with a focus on efficiency and accuracy.
Presenting danGPT: I pulled every post *ever* from @dan_abramov2 and built a RAG-based GenAI. It’s a silly side project that may be helpful. Let me know if it is, and if you’d like it open sourced and taught in a YouTube video or similar. 👉 Try it: https://t.co/MHVXWyXxDw https://t.co/GQ5vRcAeFz
If you’ve ever explored creating an AI-driven app, one concept you’ll come across is RAG (Retrieval-Augmented Generation). Would anyone be interested in a short explainer video about what it is exactly?
🐇Went on a rabbit hole to learn more about Retrieval-Augmented Generation (RAG) after reading about it for the first time in Elastic’s earnings call and a friend mentioning it to me yesterday, on RAG being used as a growing trend in LLMs. 🔴 Problem with GenAI and LLMs:… https://t.co/5fxMOG108M https://t.co/DqjH6wI0Iy