What is the primary technical rationale for applying intelligent chunking strategies that consider 'semantic boundaries' over fixed-size chunking when preparing documents for RAG?

The primary technical rationale for applying intelligent chunking strategies that consider 'semantic boundaries' over fixed-size chunking when preparing documents for RAG is to ensure that each generated 'chunk' represents a semantically coherent unit of information. In Retrieval Augmented Generation (RAG), a system retrieves relevant information from a knowledge base to augment a Large Language Model's (LLM) input, enabling more accurate and grounded responses. Chunking is the process of dividing large documents into smaller segments for efficient indexing and retrieval. Fixed-size chunking simply splits text based on a predetermined character or token count, often arbitrarily cutting off sentences or ideas. In contrast, intelligent chunking strategies i....