RAG Chunking Strategies [Top 11] | Semantic Chunking to LLM Chunking | Learn RAG from Scratch

Описание: Welcome to the FreeBirds Crew! 🚀 In this second video of our Learn RAG from Scratch Playlist, we dive deep into chunking strategies for Retrieval-Augmented Generation (RAG).

Learn:
✅ How chunking impacts LLM performance
✅ The trade-offs between long and short contexts in LLMs
✅ Step-by-step implementation of chunking using Python and LangChain
✅ 11 advanced chunking techniques explained with examples

Topics Covered:
1. Understanding context length of LLM and its impact on LLM responses
2. How to calculate tokens in any text
3. Chunking strategies: fixed size, sentence-based, semantic, token-based, and more!

Whether you're building advanced LLM applications or optimising your RAG workflows, this video is packed with practical insights and code examples to get you started.

💻 Code Walkthrough: Python examples to implement chunking for different data types.

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📂 Playlist: Learn RAG from Scratch

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