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Split content the way LLMs do

ContentChunking Tool

Split a long article into semantically meaningful chunks - the same way LLMs process your content during retrieval. Find and fix the chunks that would get lost.

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# The 2026 guide to AI search visibility

## What is GEO?
Generative Engine Optimization is the discipline of getting your brand cited inside AI assistant answers. Unlike SEO, which targets blue-link rankings, GEO targets the generative answer cards from ChatGPT, Claude, Gemini, Perplexity and Google AI Overviews. In 2026, roughly 20% of informational queries happen on AI assistants.

## How does passage retrieval work?
Large language models don't read whole articles during inference. Instead, the retrieval layer splits your content into chunks, embeds each chunk as a vector, and pulls the 3-8 nearest neighbours to a user query. That means the unit of citation is the chunk, not the page.

## What size chunk works best?
Most production retrieval stacks target 150-400 tokens per chunk, which is roughly 100-300 words. The sweet spot for editorial content is a chunk that carries one self-contained claim with supporting facts - long enough to answer the question, short enough that the entire chunk fits inside the assistant's answer card.

## How should I structure content for chunking?
Write with one idea per section. Lead with the answer, then support. Use question-shaped H2s so the retrieval layer has an obvious anchor. Avoid long transitional paragraphs that reference other sections - they chunk badly because they can't stand alone.

Mode

Chunk health

80%

4 of 5 chunks retrieval-ready

The 2026 guide to AI search visibility

0 words · Too short - will merge with next chunk at retrieval

What is GEO?

47 words · Good passage length

Generative Engine Optimization is the discipline of getting your brand cited inside AI assistant answers. Unlike SEO, which targets blue-link rankings, GEO targets the generative answer cards from ChatGPT, Claude, Gemini, Perplexity and Google AI Overviews. In 2026, roughly 20% of informational queries happen on AI assistants.

How does passage retrieval work?

46 words · Good passage length

Large language models don't read whole articles during inference. Instead, the retrieval layer splits your content into chunks, embeds each chunk as a vector, and pulls the 3-8 nearest neighbours to a user query. That means the unit of citation is the chunk, not the page.

What size chunk works best?

50 words · Good passage length

Most production retrieval stacks target 150-400 tokens per chunk, which is roughly 100-300 words. The sweet spot for editorial content is a chunk that carries one self-contained claim with supporting facts - long enough to answer the question, short enough that the entire chunk fits inside the assistant's answer card.

How should I structure content for chunking?

40 words · Good passage length

Write with one idea per section. Lead with the answer, then support. Use question-shaped H2s so the retrieval layer has an obvious anchor. Avoid long transitional paragraphs that reference other sections - they chunk badly because they can't stand alone.

How it works

Pick one of three chunking modes. By heading mirrors how heading-aware retrievers split content; the chunk is one H2/H3 section. By word count mirrors how fixed-size chunkers (LangChain, LlamaIndex) split content; you set a target and each chunk holds roughly that many words with sentence-boundary snapping. By paragraph mirrors how semantic splitters treat your paragraph breaks as chunk boundaries.

Why chunk quality matters

Chunks are the unit of citation - assistants quote chunks, not pages. A well-chunked page can carry 3-5 citations; a badly-chunked page carries zero.
Retrieval is brittle - if a chunk breaks mid-claim, the embedding no longer matches the user's intent and the whole chunk falls out of the top-K results.
Small chunks starve - a 15-word chunk loses the supporting context that makes the claim citable. Most assistants skip it.
Big chunks dilute - a 400+ word chunk has a vague embedding that matches too many queries weakly and none strongly.

Pair with

Run the Passage Optimizer on any chunk that flags as weak, the Headings Optimizer to make your H2s retrieval-friendly, and the Fact Density Analyzer to raise the citation value of each chunk. The full pattern is in our content-to-cite guide.

Related tools

Technical SEO

Headings Optimizer

Paste your content and we'll analyse the H1/H2/H3 structure - flagging missing hierarchy, weak H2s, and opportunities to reshape headings as question-form prompts that win AI citations.

Content Optimization

Passage Optimizer

Identify the 3-5 most citable passages in any piece of content and get specific rewrites to make them even more LLM-friendly.

From the Knowledge Hub

Tutorials

Write Content AI Assistants Cite

Forget classic SEO copywriting. AI assistants reward a very specific content shape - declarative answers, atomic facts, structured comparisons and source-grade prose. The exact format that gets you cited, with templates.

Want this done for you?

Ship the full GEO playbook in 14 days

Geolify GEO packages bundle every tool on this site into a 14-day done-for-you build - llms.txt, schema, entity strength, content overhaul, citations and the measurement stack. From $499.

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