AI

Semantic chunking is a smarter, meaning-based approach to breaking text into pieces for Retrieval-Augmented Generation (RAG) systems. Instead of slicing by token count, it groups sentences that belong together, keeping the logic and flow intact. This helps retrieval systems pull coherent context rather than fragmented thoughts—a crucial advantage when dealing with complex documents like contracts or financial statements. While current research shows mixed evidence on its effectiveness, the theoretical promise is clear: semantic chunking preserves context, coherence, and clarity, making RAG systems feel less mechanical and more genuinely understanding.