---
title: Chunk Extractability
description: "TL;DR: Chunk Extractability (0-100) measures how easily AI systems can pull self-contained content pieces from your pages. Pages with >70% lists/tables score 3x higher on..."
url: "https://suprmind.ai/hub/methodology/chunk-extractability/"
published: "2025-12-26T14:25:39+00:00"
modified: "2026-05-01T12:37:16+00:00"
author: Radomir Basta
type: methodology
schema: WebPage
language: en-US
site_name: Suprmind
---

# Chunk Extractability

## What is Chunk Extractability?

>**Chunk Extractability**measures how easily RAG (Retrieval Augmented Generation) systems can extract self-contained, meaningful content chunks from your pages. AI systems don’t read pages top-to-bottom—they grab specific chunks that answer specific questions.
> Think of it as the difference between**Lego blocks**(modular, reusable) and a**solid blob**(can’t break apart without losing meaning).
>**Key Finding:**Pages scoring 80/100 on Chunk Extractability are cited 3x more often than narrative-heavy pages with the same information (FAII crawler analysis, N=1,000 pages).

## How Chunk Extractability is Calculated

Chunk Extractability is scored based on structural elements that enable clean extraction:

| Element | Points | Target |
| --- | --- | --- |
|**H2-H3 Hierarchy**| 30 points | Questions as headers (“What is X?”, “How to Y?”) |
|**Lists & Tables**| 40 points | >70% of body content in structured format |
|**Schema Markup**| 20 points | DefinedTerm, FAQPage, HowTo schemas |
|**Paragraph Length**| 10 points |

---

*Source: [https://suprmind.ai/hub/methodology/chunk-extractability/](https://suprmind.ai/hub/methodology/chunk-extractability/)*
*Generated by FAII AI Tracker v3.3.0*