The Art of Chunking in AI Systems: Foundation and Core Techniques

Description:

*The Art of Chunking in AI Systems: Foundation and Core Techniques* is a comprehensive guide to chunking in Retrieval-Augmented Generation (RAG) systems. It covers fixed-length, character-based, token-based, sliding-window, document-specific, semantic, code-aware, hierarchical, and agentic chunking approaches. The book discusses the principles, advantages, limitations, implementation considerations, and practical applications of these techniques, with guidance for selecting and evaluating chunking strategies for real-world AI systems.

subjects: Artificial Intelligence,  Retrieval-Augmented Generation,  RAG,  Information Retrieval,  Large Language Models,  LLMs,  Natural Language Processing,  NLP,  Machine Learning,  Chunking,  Semantic Chunking,  Vector Search,  Generative AI