Retrieval Augmented Generation for AI Apps
summary
Claims that a basic generative AI app can be live in 24 hours if using a self‑hosted LLM, but a full RAG pipeline takes longer. Implementation uses Amazon SageMaker with the SageMaker Python SDK to deploy a model to an inference endpoint, stores PDFs in S3 via a bash upload, extracts text with Textract and processes chunks into embeddings, and wires LangChain to run retrieval + generation. Naive RAG (index/retrieve/generate) shows low precision/recall and hallucinations; advanced RAG adds sliding‑window, segmentation and metadata to improve results.
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chapters (4)
- 0:00intro
- 0:20preamble
- 0:31about speakers
- 1:21can we build a generative ai-powered application in 24 hours?
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