AI Agent Chunking Strategy 2026

Blocks too large lose precision; blocks too small lack context. Document chunking is the most underestimated technique in RAG—it determines what the LLM can see.

💡 What You Will Learn

Blocks too large lose precision; blocks too small lack context. Document chunking is the most underestimated technique in RAG—it determines what the LLM can see.

📜 Table of Contents

• Three Chunking Methods
• Optimal Chunk Sizes for Different Document Types
• Summary
• Real Test Data
• Real Project Experience

Anyone who has worked with RAG knows: with the same documents and the same queries, different chunking methods can lead to wildly different answer quality.

Three Chunking Methods

1. Fixed-size (most common) 500 characters per chunk with a 50-character overlap. Simple but crude.

2. Semantic chunking Split naturally by paragraphs/headings. Each chunk is a complete unit of meaning.

3. Recursive chunking Start with large chunks, then recursively split long ones further. Best results overall.

Optimal Chunk Sizes for Different Document Types

Summary

There's no one-size-fits-all optimal chunking strategy. Try all three methods on your dataset, measure recall, and pick the best one. Generally, recursive chunking delivers the best results but is the slowest, while fixed-size is the most stable.

Real Test Data

Retrieval performance across three chunking methods on the same 10-page product document:

Recursive chunking is a bit slower, but accuracy jumps by 13 percentage points—worth it.

Real Project Experience

My last project used recursive chunking. The product doc was 42 pages—split into large chunks by chapter, then recursively subdivided any chapter over 1,000 characters. Ended up with 35 chunks and 85% retrieval accuracy.

Compared to fixed-size 500-character chunking: more chunks (42) but lower accuracy (72%). Because fixed-size chunking cut through complete technical explanation paragraphs, leaving retrieval with only half a passage.

Recommended approach: first chunk by the document's own heading structure, then recursively split long chunks. This strategy performs best on most datasets.