RAG Explained + Build a RAG App From Scratch in Python using LLM | Chapter 08
Summary
This video teaches the core concepts of retrieval-augmented generation and demonstrates how to build a complete RAG application from scratch in Python using a Groq language model. The tutorial covers a seven-step pipeline including document ingestion, token-based text chunking, vector embedding generation, in-memory storage, similarity search, prompt augmentation, and response generation.
Summary generated by brickster.ai from the video transcript.
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