Semantic Chunking

What is Semantic Chunking?

The way your content is split into chunks determines the quality of AI responses generated from it. If a RAG system cuts mid-thought, the extracted answer becomes unusable. For your website, this means: structure your content in clearly delineated, thematically complete paragraphs — this improves not only readability but also the chances of AI systems correctly using your content as a source.

Semantic Chunking is an intelligent method for document segmentation in RAG systems that uses content rather than arbitrary boundaries as the splitting criterion. While fixed-size chunking blindly cuts text every 500 tokens — tearing apart connected thoughts — Semantic Chunking recognizes where thematic transitions occur and sets boundaries accordingly.

The technique typically works via embedding comparisons: consecutive sentences are converted into vectors and their semantic similarity is compared. As long as adjacent sentences address similar topics, they belong to the same chunk. When similarity drops below a threshold — meaning a topic change has occurred — a new chunk begins. The result is text sections that each cover a coherent theme.

For companies operating RAG-based systems, Semantic Chunking delivers noticeably better answer quality than fixed chunking. The retrieved text sections are content-complete and thematically focused — ideal conditions for Cross-Encoder Reranking and Hybrid Search. Those who need even more control can advance to Agentic Chunking, where an LLM controls the segmentation.