The Mathematics of Semantic Chunking: Optimizing Retrieval Density

In the frantic gold rush of 2024 to build Retrieval-Augmented Generation (RAG) applications, we committed a collective sin of optimization. We obsessed over the model (GPT-4 vs. Claude 3.5), we obsessed over the vector database (Pinecone vs. Weaviate), and we obsessed over the prompt.

But we ignored the input.

Most RAG pipelines today still rely on a primitive, brute-force method of data ingestion: Fixed-Size Chunking. We take a document, we slice it every 512 tokens, we add a 50-token overlap, and we pray that we didn’t cut a critical sentence in half.