How RAG is Redefining AI Capabilities
summary
Shows how Retrieval-Augmented Generation is built end to end: document ingestion (offline) and retrieval (online) with parsing, chunking, embeddings and a vector store; contrasts a quick “naive RAG” (250-char chunks, PIP PDF or unstructured.io, N8N) with a production pipeline that uses semantic chunking, GPT‑4 vision or Gemini 1.5 for table/image parsing, and vector DB retrieval tuned between cosine similarity vs BM25. Covers evaluation (ROUGE/BLEU, hallucination rates, latency and cost) and recommends starting simple then hardening for scale.
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chapters (9)
- 0:00AI agents and RAG intro
- 1:49Introduction
- 2:55AI agents and components
- 6:26What is RAG
- 8:31RAG ingestion and chunking
- 11:38RAG implementation types
- 14:09Advanced RAG parsing
- 16:38RAG retrieval methods
- 19:30RAG advantages
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