Chunking Strategies: Fixed, Sentence, Semantic and Document-Aware

Chunking is the most underrated lever in RAG quality. The same documents, embedded with the same model, retrieved against the same query, can return wildly different results based purely on how they were chunked.

Why it matters

Fixed-size chunks split sentences. Sentence chunks lose context. Semantic chunks are computationally expensive. Document-aware chunks need per-format parsers. Picking one strategy for all documents is the default - and the default is usually wrong.

How Bhogar AI approaches it

Bhogar AI auto-detects document type and applies the right strategy: code gets AST-aware chunks, markdown gets heading-aware chunks, PDFs get layout-aware chunks, and conversations get turn-aware chunks. You can override per knowledge base.

• AST-aware chunking for source code
• Heading-aware chunking for Markdown and HTML
• Layout-aware chunking for PDFs with tables and figures
• Turn-aware chunking for chat transcripts and call recordings
• Per-KB override and chunk-quality scoring

What you get

Customers switching from one-size-fits-all chunking to auto-selected strategies see retrieval recall lift 15-35 points, with no other code changes.