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 identify and respect 'semantic boundaries,' which are natural logical divisions in text, such as ends of paragraphs, sections, or complete sentences, where one distinct unit of meaning ends and another begins. By respecting these boundaries, each chunk is more likely to contain a complete thought, concept, or answer to a potential question. This approach offers two critical technical advantages: improved retrieval relevance and enhanced generation quality. For retrieval relevance, when a query is made, the system uses embeddings, which are numerical vector representations of text capturing its meaning, to find the most similar chunks. If a chunk contains a complete semantic unit, its embedding accurately represents that unit, leading to more precise similarity matching and ensuring that all necessary context related to the query is present within the retrieved chunk. A fixed-size chunk, by splitting a coherent idea, might lead to fragmented information across multiple chunks, making individual chunks less informative and harder to retrieve accurately for a specific query. For enhanced generation quality, a semantically coherent retrieved chunk provides the Language Model with a complete and self-contained piece of information. This complete context significantly reduces the likelihood of the LLM generating incomplete, inaccurate, or fragmented answers, a phenomenon often referred to as 'hallucination,' because the LLM is not working with partial or ambiguous information. For example, if a definition of a complex term is split across two fixed-size chunks, neither chunk alone provides sufficient context for the LLM to generate an accurate explanation, whereas an intelligently chunked segment containing the entire definition would allow for a precise response.