What is RAG ?
RAG stands for Retrieval-Augmented Generation, a framework commonly used in natural language processing (NLP) and artificial intelligence (AI). It combines the strengths of retrieval-based models and generative models to enhance the generation of text.
Welcome to our easy explanation of Retrieval-Augmented Generation (RAG) systems! In this video, we dive into the cutting-edge technology that combines the strengths of retrieval and generation models to revolutionize natural language processing (NLP) and Generative AI.
Large language models (LLMs) are known for generating impressive answers, but their responses are limited by the data they were trained on. Over time, this can lead to incomplete or even incorrect answers. To enhance the accuracy of LLM outputs, we can use a method called "retrieval-augmented generation" (RAG).
🚀 What You'll Learn:
Introduction to RAG Systems: Learn the basics of RAG and its significance in AI and NLP.
Vector Embeddings: Explore various embedding techniques, such as Word2Vec, GloVe, FastText, ELMo, BERT, SBERT, T5, and DPR.
How RAG Works: Learn about the integration of retrieval and generation components and how they work together to provide accurate and context-aware responses.
📢 Join the Conversation:
Have questions or thoughts about RAG systems? Drop a comment below, and let’s discuss!
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00:00 Introduction
00:47 Vector Embeddings
02:03 Vector Search
02:45 Retrieval-Augmented Generation
