Mastering Text Chunking: From Sliding Windows to Token-Based Approaches

This guide explores chunking methodologies fundamental to RAG systems: sliding window chunking for capturing context through strategic overlap, and token-based chunking for handling rate-limited embedding models. The choice between approaches directly impacts token consumption, embedding quality, and the relevance of retrieved results in production systems. Understanding when and how to apply each technique is critical for optimizing both cost and accuracy in retrieval-augmented applications.