What the RAG? Demystifying Retrieval Augemented Generation
summary
Claims a working houseboat booking chatbot built end-to-end: chunking PDFs into 2k–5k token pieces, generating embeddings, storing them in a vector store (Astra or Pinecone or Postgres), retrieving the five nearest neighbors, composing a prompt and answering with calls GBT40 mini. Notes concrete limits: context window ≈128,000 tokens and Gemini 1.5 Pro ≈1,000,000 tokens. Demo wires a reservation tool that issues an HTTP POST and a WhatsApp integration that shows live requests, and an abstraction layer called Twilio AI assistance removes vector DB and orchestration pain.
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chapters (9)
- 0:00Introduction to Retrieval Augmented Generation
- 3:02Chatbots need RAG for reliability
- 6:37Expensive ways to expand LLM
- 7:19How RAG works step by step
- 12:21Hands-on RAG chatbot demo
- 17:15Proof of concept vs production
- 19:20Twilio AI Assistance in production
- 20:23AI assistant demo with tools
- 26:56Simplified architecture & closing
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