Definition

What is Chunking?

Last updated

Chunking is the process of splitting documents into smaller passages so they can be embedded, retrieved, and delivered to an AI model's context window.

Chunk boundaries decide whether a model receives a complete, self-contained idea or a fragment cut off from the information it needs. That makes chunking strategy a core context engineering decision that directly affects retrieval accuracy and hallucination rates.

Related RAG (Retrieval-Augmented Generation) Semantic Search Structured Context Context Window Context Engineering

Further reading

Articles about Chunking

Chunking Strategies Decide What Context AI Sees

Chunking strategies decide which context reaches your AI. How fixed-size, semantic, and late chunking change retrieval accuracy, with 2026 benchmark data.

All terms

View full glossary
Agent Drift Agent Reliability AI Agent AI Hallucination AI Second Brain Chunking Context as a Service Context Bloat Context Compression Context Container Context Drift Context Engineering Context Offloading Context Poisoning Context Portability Context Pruning Context Rot Context Window Epistemic Provenance Fine-Tuning Knowledge Graph MCP Resources MCP Server MCP (Model Context Protocol) Multi-Agent System Prompt Caching Prompt Engineering RAG (Retrieval-Augmented Generation) Semantic Search Structured Context Tool Poisoning Wire

Put context into practice

Create your first context container and connect it to your AI tools in minutes.

Create Your First Container