GEO Checker

TL;DR: The GEO Checker evaluates how likely a piece of content (URL or text) is to be understood, trusted, and cited by generative AI engines. It scores against four core dimensions (Authority, Relevance, Structure, User Value) and provides concrete fix suggestions for every deduction.

Two check modes

Both modes produce the same output format, but URL check additionally evaluates technical dimensions (structured data, heading hierarchy, accessibility, SSR status, etc.), while text check focuses more on semantics and organization.

Scoring dimensions

Authority

Evaluates whether the content's sources, author background, and citation quality are sufficient to make AI engines "willing to cite."

• Are author identity, byline, and credentials clear?
• 3–5 named citations per 1,000 words, and they must link to specific article URLs — not the site homepage (industry research shows this set of citation rules can lift AI visibility by about 40%)
• Do data / conclusions carry publication dates, version info, and traceable sources?
• Is the content linked or mentioned by external authoritative sites?

Relevance

Measures whether the content truly answers the target query rather than stuffing keywords.

• Semantic alignment between topic and target keywords
• Coverage of user intents (informational / navigational / transactional / comparative)
• Coverage of related sub-questions and context
• Does the article open with a "quick answer / TL;DR" block so AI can extract the answer from above the fold?
• Are irrelevant or redundant paragraphs avoided?

Structure

AI engines parse structured content more easily. This dimension evaluates a page's "machine readability."

• Is the heading hierarchy sensible (unique H1, consistent H2/H3 nesting)?
• Is key information presented in lists, tables, definition blocks, and other structured forms?
• JSON-LD triple stacking: use Article + FAQPage + HowTo (or Product + Review) together on appropriate pages — don't settle for just one
• Server-side rendering (SSR): AI crawlers generally don't execute JS, so key content must appear in the initial HTML. See llms.txt and AI Crawlers
• Are paragraph lengths and transitions reasonable?

User Value

The hardest to quantify but the most important: is the content actually "useful" to real users?

• Does it provide actionable steps, templates, code, or examples?
• Does it include original insight rather than restating others' content?
• Does it anticipate and answer the reader's follow-up questions (FAQ sections score extra)?
• After reading, can the user "skip one fewer article"?

Output report

Each check produces a report containing:

1. Total score (0–100) — weighted sum across four dimensions
2. Per-dimension score — to pinpoint weak areas
3. Deduction details — every rule that hit, its deduction value, and a problem description
4. Fix suggestions — specific rewrites or additions for each deduction
5. Priority order — high impact × easy fix items come first

Recommended check frequency

The 2026 industry consensus is high-value content should be refreshed every 7–14 days (even just a timestamp update + small addition); otherwise AI engines' freshness signal decays quickly and citation priority drops.

Pre-publish checklist

Tick each item; if anything is missing, go back and fix it before publishing:

• Article opens with a standalone TL;DR / quick-answer block (extractable as a single snippet)
• Heading hierarchy is unique and consistent (single H1, sensible H2/H3 nesting)
• 3–5 named citations per 1,000 words, linking to specific article URLs
• At least one original data point / case / diagram
• Author byline + brief credentials
• JSON-LD: at least Article, ideally stacked with FAQPage / HowTo
• FAQ section (covers the reader's next question)
• curl -A "GPTBot" <URL> confirms SSR output includes the body

Related reading

• GEO Core Concepts
• Content Strategy Best Practices
• Performance Analysis
• llms.txt and AI Crawlers