Developing a Custom RAG System
summary
How to build a custom Retrieval-Augmented Generation (RAG) system for augmented intelligence, covering the technologies and components needed to combine vector search, document retrieval, and LLMs. It explains practical choices for embeddings, vector stores, retrieval pipelines, and how to integrate those elements to serve context-aware generation in applications.
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chapters (17)
- 0:00Introduction
- 0:21What is RAG?
- 2:30LLM limits as search engines
- 5:21Search engine strengths/weaknesses
- 6:46Retrieval Augmented Generation principle
- 9:37Large-scale RAG examples
- 14:57Demo on All.site
- 25:06RAG vs ChatGPT real cases
- 31:02Key RAG system components
- 33:51QA vs RAG
- 35:13Vector search optimizations
- 38:46Citing sources in answers
- 40:31Model and tool choices
- 41:33Handling large documents
- 43:22Query rewriting improvements
- 48:36Further resources
- 49:17Q&A
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