LLM Optimization Checker

As AI-powered search engines like ChatGPT, Perplexity, Claude, and Google AI Overviews become primary information discovery tools, optimizing your content for Large Language Models (LLMs) is no longer optional. This practice, known as Generative Engine Optimization (GEO), ensures your content gets discovered, cited, and recommended by AI systems.

What is LLM Optimization?

LLM optimization is the process of structuring and enhancing your web content so that AI models can easily parse, understand, and cite it. Unlike traditional SEO which focuses on search engine crawlers and ranking algorithms, GEO focuses on making content machine-readable, authoritative, and directly answerable.

When a user asks ChatGPT "What is the best project management tool?" or Perplexity "How does content marketing work?", the AI synthesizes information from multiple sources. Content that is well-structured, factual, and properly marked up has a significantly higher chance of being cited in these AI-generated responses.

The 13 Parameters We Check

Our LLM Optimization Checker evaluates your website across 13 carefully selected parameters, divided into two categories: 9 deterministic (technical) checks and 4 AI-evaluated content quality checks.

Technical Signals (9 Deterministic Parameters)

1. Structured Data (Weight: 15) — JSON-LD structured data is the single most important signal for AI discoverability. Schema types like Article, BlogPosting, FAQPage, Organization, and Person help AI models instantly understand what your content is about, who wrote it, and when it was published. Without structured data, AI models must infer context from raw HTML, which is significantly less reliable.

2. Content Freshness (Weight: 10) — AI models heavily prefer recent, up-to-date content. Dates are extracted from JSON-LD (dateModified, datePublished), meta tags (article:modified_time), HTTP headers (Last-Modified), and HTML time elements. Content updated within the last 90 days scores significantly higher than content that hasn't been touched in over a year.

3. AI Crawler Access (Weight: 10) — If your robots.txt blocks AI crawlers like GPTBot (OpenAI), ClaudeBot (Anthropic), PerplexityBot, or Google-Extended, your content simply won't appear in AI-generated responses. This is a binary signal with massive impact — blocked means invisible.

4. Semantic HTML (Weight: 8) — Using HTML5 semantic elements (article, section, main, aside, nav, figure) instead of generic divs helps AI parsers understand content structure and importance hierarchy. A page with a clear main content area wrapped in <article> and <section> elements is far more parseable than a div soup.

5. Heading Hierarchy (Weight: 8) — A proper heading structure with a single H1, followed by H2s and H3s without level skipping, creates a clear content outline that AI models can use to identify topics and subtopics. Multiple H1s or jumping from H1 to H4 signals poor structure.

6. Content Length (Weight: 7) — Comprehensive content (1,500+ words) provides enough depth for AI models to extract detailed, nuanced answers. Thin content under 500 words rarely provides enough substance for AI citation. However, length alone doesn't guarantee quality — the content must also be well-structured and authoritative.

7. Statistics Density (Weight: 5) — AI models prefer content with specific, quantitative data points — percentages, dollar amounts, measurements, and named quantities. Content that says "revenue increased by 47% to $2.3 million" is far more citable than "revenue increased significantly."

8. Source Citations (Weight: 5) — External links to authoritative sources, cite elements, and reference sections signal that your content is well-researched. AI models use citation density as a proxy for content quality and reliability.

9. E-E-A-T Signals (Weight: 7) — Experience, Expertise, Authoritativeness, and Trustworthiness signals — author bylines, Person schema, Organization schema, and author attribution — help AI models assess content credibility. Google explicitly uses E-E-A-T in its quality guidelines, and AI models increasingly rely on similar signals.

AI Content Quality (4 AI-Evaluated Parameters)

10. Direct Answers (Weight: 8) — Content that leads with direct, factual answers rather than burying information in marketing fluff is significantly more likely to be cited by AI. An answer-first format ("The best time to post on Instagram is 11am EST on Tuesdays") is far more citable than a gradual build-up.

11. Summary/TL;DR Presence (Weight: 7) — A clear summary, TL;DR, or Key Takeaways section at the beginning or end of your content gives AI models a pre-packaged answer they can cite directly. This is one of the easiest wins for LLM optimization.

12. Content Clarity (Weight: 5) — Short paragraphs, clear transitions, and logical content flow make it easier for AI models to parse and understand your content. Long, dense paragraphs with poor flow reduce comprehension accuracy.

13. List & Format Quality (Weight: 5) — Well-structured bulleted lists, numbered steps, and comparison tables present information in formats that AI models can easily extract and present. A bulleted list of "Top 5 Benefits" is more AI-friendly than the same information buried in prose.

How the Scoring Works

Our hybrid scoring system combines deterministic analysis (computed programmatically from HTML) with AI-powered content quality evaluation (using Google's Gemma 3 model). The 9 technical parameters are worth up to 75 points, and the 4 AI parameters are worth up to 25 points, for a total maximum of 100.

If the AI service is temporarily unavailable, you still get a reliable score from the 9 deterministic parameters (up to 75 points). This ensures you always get actionable insights regardless of AI service availability.

Quick Wins for Better AI Discoverability

• Add JSON-LD structured data — At minimum, add Article and Organization schema to your content pages
• Allow AI crawlers — Check your robots.txt and ensure GPTBot, ClaudeBot, and PerplexityBot are not blocked
• Update your content — Add visible dates and keep dateModified current in your JSON-LD
• Use semantic HTML — Wrap content in article, section, and main elements instead of divs
• Fix your heading hierarchy — Single H1, proper H2/H3 structure, no level skipping
• Add a TL;DR section — Summarize key points at the top or bottom of each article
• Include author bylines — Add author names with Person schema for E-E-A-T credibility
• Use specific data — Replace vague claims with specific statistics and numbers
• Lead with answers — Start sections with direct factual statements

The Future of GEO

As AI search continues to grow, LLM optimization will become as fundamental as traditional SEO. Websites that invest early in structured data, content quality, and AI-friendly formatting will have a significant competitive advantage. The sites that AI models learn to trust and cite consistently will build a compounding advantage over time.

Unlike traditional SEO where algorithm updates can suddenly shift rankings, AI citation tends to be more stable — once AI models recognize your site as authoritative on a topic, that trust compounds with each new piece of well-structured content you publish.

Frequently Asked Questions

Is GEO the same as traditional SEO?

No. Traditional SEO optimizes for search engine ranking algorithms (PageRank, content relevance, backlinks). GEO optimizes for AI comprehension and citation. There is significant overlap — well-structured, authoritative content benefits both — but GEO has unique requirements like AI crawler access, answer-first formatting, and specific structured data types.

Should I block AI crawlers?

This depends on your business model. Publishers who monetize through advertising may want to limit AI access to prevent content from being summarized without driving traffic. However, for most businesses, allowing AI crawlers increases discoverability and brand visibility. If AI users discover your brand through ChatGPT or Perplexity, many will visit your site directly for the full experience.

How often should I check my LLM optimization?

We recommend checking after any significant content update, structural change to your website, or robots.txt modification. Monthly checks are a good baseline. Pay special attention when you add or remove structured data, change your content management system, or update your robots.txt file.

What's the minimum score I should aim for?

A score of 70+ indicates good AI discoverability. Scores above 85 are excellent. Below 50 suggests significant optimization opportunities. Focus first on the highest-weight parameters: structured data (15), content freshness (10), AI crawler access (10), and direct answers (8).