What does the chunking check look for?
It measures how extractable your content is — how easily a machine could lift a standalone, meaningful passage. Markers of good chunking include:
- Granular sections — frequent, descriptive H2/H3 headings breaking the content into focused units.
- Short paragraphs — a few sentences each, one idea per paragraph, rather than dense blocks.
- Self-contained units — each section reads sensibly even if pulled out of the page.
- Supporting structure — lists and tables where the content is enumerable.
Well-chunked content passes; partially chunked content with some long blocks is a warning; a wall of text with little structure is a fail.
How is it evaluated, and how is it scored?
GEObubbly measures section granularity, average paragraph length, and whether sections stand alone (roughly within the ~500-token chunks LLMs work in). It's a core GEO / LLM Readiness check worth 4 points, because extractable structure directly determines whether your content fits cleanly into an engine's quoting window.
Why chunking and extractability matter for GEO
Large language models process text in chunks — roughly ~500-token segments — and they quote self-contained passages. Content that's well-chunked gives them clean, liftable units: break it into granular sections with descriptive headings, keep paragraphs to a few sentences with one idea each, and make every section read as a standalone unit that makes sense even when pulled out of the page. A wall of text does the opposite: it forces the model to choose between quoting an awkward fragment and skipping the page, and it usually skips. This is structural, not stylistic fluff — it changes whether your content fits an engine's extraction window at all. It also makes the page far easier for humans to skim, improving the engagement signals that feed back into rankings. Chunking works hand in hand with answer-first writing and lists and tables across GEO / LLM Readiness.