Advanced RAG with LangChain | Multi-Document Retrieval, Chunking & Re-ranking

📘 GenAI Production Blueprint
How I think about production-grade GenAI systems beyond demos
👉 https://gauranggupta1712.gumroad.com/l/xjlaky

In this video, we go beyond basic RAG and build an advanced Retrieval-Augmented Generation (RAG) pipeline using LangChain.

You’ll learn how real-world RAG systems work and how to improve answer quality using better chunking, multi-document retrieval, and retrieval optimization techniques.

This is NOT a beginner RAG tutorial. If you already know embeddings and basic vector search, this video will level you up.

📌 What you’ll learn:
- Advanced RAG architecture and workflow
- Chunking strategies that actually work
- Multi-document retrieval using vector databases
- Improving retrieval quality before generation
- How to structure RAG code for production-ready projects

🔗 Code for the video
https://github.com/Gaurang-gupta/langchain-course/tree/main/Lecture-07

🧠 Assignment (Important)
I’ve added a hands-on assignment for this video.
👉 Check the GitHub link in the description to implement Advanced RAG yourself.
This assignment is highly recommended if you want resume-worthy experience.

🔗 GitHub Repository:
https://github.com/Gaurang-gupta/langchain-course/tree/main/Lecture-07_assignment

📌 Prerequisites:
- Basic understanding of RAG
- Python
- LangChain fundamentals
- Vector databases (FAISS / Chroma)

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Видео Advanced RAG with LangChain | Multi-Document Retrieval, Chunking & Re-ranking канала Projects with Gaurang