Advanced Retrieval Augmented Generation (RAG) Deep Dive

This session breaks down advanced RAG patterns, from chunking and embeddings to similarity metrics, vector storage, retrieval strategies, and LLM chain responses. We’ll also explore streaming, chat history, and where GraphRAG fits in.

You’ll leave with a clear blueprint for building accurate, optimized RAG pipelines for personal or enterprise-scale scenarios. Guided project: You’ll build a local serverless RAG chat app using CodeTours covering architecture, ingestion, Cosmos DB/FAISS storage, retrieval, and response generation. By the end, you’ll have a working repo plus practical intuition for tuning chunking, similarity, and evaluation.

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The Java-Script AI Build-a-thon

📌 This session is a part of a series! Learn more here