Explore advanced chunking strategies for optimizing data in RAG applications in this comprehensive 14-minute video tutorial. Learn about various methods including character splitting, recursive splitting, document-based, semantic, and agentic chunking to improve AI response accuracy. Follow step-by-step instructions for setting up and integrating these techniques with your RAG application, covering everything from data division and embeddings to storage in vector databases. Gain insights into different levels of chunking strategy and understand why high-quality chunking is crucial for producing accurate AI responses. Perfect for both beginners and advanced users looking to enhance their data processing techniques in AI applications.
Chunking Strategies in RAG - Optimizing Data for Advanced AI Responses
Overview
Syllabus
- Introduction to Chunking Strategies in RAG
- Detailed Tutorial on Various Chunking Methods
- Setup Instructions for Chunking Environment
- Code Walkthrough for Character Text Splitting
- Implementing Recursive Character Text Splitting
- Exploring Document Text Splitting Techniques
- Introduction to Semantic Chunking with Embeddings
- Advanced Agentic Chunking for Optimised Grouping
- Conclusion
Taught by
Mervin Praison
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