---
type: concept
title: "Context-Aware Chunking"
description: "Splitting documents along semantic boundaries and enriching chunks with surrounding context to improve retrieval quality."
tags: [chunking, rag, context]
videos: [every-rag-strategy-explained-in-13-minutes-no-fluff, super-special-livestream-this-weekend-live-ai-agent-build, turn-any-file-into-llm-knowledge-in-seconds, your-ultimate-n8n-rag-ai-agent-template-just-got-a-massive-upgrade, turn-any-website-into-llm-knowledge-in-seconds-evolved, the-future-of-rag-is-agentic-learn-this-strategy-now]
created: 2026-07-21
updated: 2026-07-21
---

# Context-Aware Chunking

Context-aware chunking splits a document along its natural semantic and structural boundaries rather than at arbitrary character counts, keeping related ideas intact within each chunk. Instead of a blind fixed-size split, "we're using an embedding model to find the natural boundaries in our document so that we can split and it's going to be free and fast and we will maintain our document structure" [0:07:45]. The goal is to never sever an idea mid-thought: "if we have to split a document at any given point we don't want to split it in the middle of a paragraph or the middle of a sentence or a code example we want it to preserve that" [0:13:07].

## Why it matters

A naive character split destroys meaning at the seams. As Cole describes the problem, "we're now splitting ideas in our document between different chunks that we'd want to actually have grouped together" [0:03:48]. When a chunk contains half of one concept and half of another, its embedding represents neither well, and retrieval degrades. Context-aware chunking keeps "the distinct sections all kept together in those chunks. You don't lose that contextual information" [0:20:42], which directly lifts retrieval accuracy for hard cases like YouTube transcripts and long documentation pages.

## How it works

There are several flavors along a cost-versus-power spectrum. The simplest is structure-based splitting: for markdown, "I split based on headers... I chunk all of the documents based on the primary headers... and then I also chunk based on the subsections" [0:20:11], and because crawlers return markdown, the code-block syntax is a reliable signal for where a section starts and ends. Next is semantic (hybrid) chunking, "using an embedding model to define the semantic similarity between the different paragraphs and sentences that we have in our document" and "letting the embedding model decide what goes into each bite-sized piece of information to keep all the similar ideas together" [0:13:35]. The most flexible and expensive is agentic chunking, "leveraging the intelligence of a large language model to help us determine how to chunk our documents" [0:03:31], which Cole calls "the most flexible and the most powerful," though semantic chunking is free and fast by comparison.

## Builds on

- [Chunking](./chunking.md) - the base preprocessing step this refines with boundary awareness.
- [Vector Embeddings](./vector-embeddings.md) - the embedding model that scores semantic similarity between candidate splits.

## Contrasts with

- [Late Chunking](./late-chunking.md) - the inverse ordering: embed the whole document first, then split, rather than finding boundaries before embedding.
- [Structured vs Unstructured Data](./structured-vs-unstructured-data.md) - this technique is for prose; structured tables want a different representation entirely.

## Related

- [RAG](./rag.md) - the pipeline whose accuracy this ingestion step raises.

## Tools

- [Docling](../entities/tools/docling.md) - "a library that I use in Python that makes it very easy to implement hybrid chunking, which is a form of contextaware chunking" [0:08:09].
- [Crawl4AI](../entities/tools/crawl4ai.md) - returns markdown, so header and code-block boundaries are explicit and easy to split on.

## Sources

- [Every RAG Strategy Explained in 13 Minutes (No Fluff)](../sources/every-rag-strategy-explained-in-13-minutes-no-fluff.md) - "[0:08:09] Docling is a library that I use in Python that makes it very easy to implement hybrid chunking, which is a form of contextaware chunking"
- [Turn ANY File into LLM Knowledge in SECONDS](../sources/turn-any-file-into-llm-knowledge-in-seconds.md) - "[0:13:35] We are letting the embedding model decide what goes into each bite-sized piece of information to keep all the similar ideas together."
- [Your ULTIMATE n8n RAG AI Agent Template just got a Massive Upgrade](../sources/your-ultimate-n8n-rag-ai-agent-template-just-got-a-massive-upgrade.md) - "[0:08:44] I tried semantic chunking which is actually using embedding models instead of large language models... But man, agentic chunking is just the most flexible and the most powerful."
- [The Future of RAG is Agentic - Learn this Strategy NOW](../sources/the-future-of-rag-is-agentic-learn-this-strategy-now.md) - "[0:13:07] if we have to split a document at any given point we don't want to split it in the middle of a paragraph or the middle of a sentence or a code example we want it to preserve that"
- [Turn ANY Website into LLM Knowledge in Seconds - EVOLVED](../sources/turn-any-website-into-llm-knowledge-in-seconds-evolved.md) - "[0:20:42] the distinct sections are all kept together in those chunks. You don't lose that contextual information"
- [Super Special Livestream this Weekend! (LIVE AI Agent Build)](../sources/super-special-livestream-this-weekend-live-ai-agent-build.md) - "[0:03:48] this chunking strategy is actually going to be super critical for accurate rag for a YouTube transcript"
