In this article, you will learn how to choose the right chunking strategy for a retrieval-augmented generation system by working through a structured decision tree tied to the properties of your documents and retrieval task.

Topics we will cover include:

What each major chunking strategy assumes about your documents, and when those assumptions hold.

A six-question decision tree that maps concrete document and retrieval conditions to specific chunking strategies.

The failure signals that indicate a chosen strategy is not working, and the fixes that tend to resolve them.

Introduction

When you are building a retrieval-augmented generation system, you often work with documents that are larger than what an embedding model or LLM context window can accommodate, so you need to split them into smaller chunks. Chunk size and overlap may seem like straightforward configuration choices, but the way you set those boundaries directly affects retrieval.…