Fixed-Size Chunking in RAG Systems Strips Context and Degrades Retrieval Quality

A growing critique among developers argues that standard retrieval-augmented generation (RAG) pipelines damage document meaning by splitting text into fixed-size chunks regardless of structure. When documents like technical specs are cut at arbitrary token counts, headings, explanations, and code examples become disconnected fragments in vector space. This means a retrieval system may return a high-similarity chunk — such as a code block — while the reasoning and context behind it remain indexed separately and out of reach. The author proposes shifting focus from optimal chunk size to logical boundaries, keeping together units like a decision with its rationale or an API endpoint with its parameters. The core argument is that document structure — headings, sections, lists — carries meaning, not just formatting, and destroying it before indexing leads to retrieval that technically succeeds but practically fails.