Illustrated Guide to RAG - Retrieval Augmented Generation

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You can find the RAG Lecture at the Weekly Lectures page, linked in the first email you receive!
Attention & Transformers: • Illustrated Guide to A...
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Retrieval-Augmented Generation (RAG) is a method in natural language processing (NLP) that combines retrieval-based and generation-based approaches to improve the quality and relevance of generated text. In RAG, a retrieval component first searches a large database or corpus to find relevant documents or passages based on a given query. These retrieved documents are then fed into a generation model, which uses them as context to produce more accurate and contextually appropriate responses. This hybrid approach leverages the strengths of both retrieval (precision and factuality) and generation (fluency and flexibility) to enhance the overall performance of tasks such as question answering, summarization, and conversational AI.