LLM audit

Use LLM audit to check if your site is ready for AI discovery, then turn the findings into concrete fixes for engineering and SEO.

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Questions this page should answer

1. Is AI-readiness improving or stalling over time?
2. Which technical area is holding us back right now?
3. What should we fix first this sprint?

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Before you analyze

• Keep the same date range you use in AI Search pages.
• Compare the newest run with at least one prior run.
• Open the newest run detail before creating tickets.

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What this page gives you

• Five top scores: Performance, Accessibility, Best practices, SEO, Content
• Audit history so you can compare runs over time
• A full report preview with crawlability, schema, content structure, NLP, and Lighthouse-based diagnostics

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How to read the list view

• Performance: speed and technical execution quality
• Accessibility: structural clarity for users and machines
• Best practices: implementation hygiene and safety checks
• SEO: search-engine technical health
• Content: clarity and structure of on-page content for model understanding

How to interpret the top row:

• Low Content with high SEO usually means classic SEO is fine, but model parsing quality is weak.
• Low Performance can reduce crawl reliability and increase processing friction.
• Flat scores for months usually mean no active technical improvement cycle.

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How these scores are calculated (simple)

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Category score (Performance, Accessibility, Best practices, SEO, Content)

Category scores are weighted pass rates of checks in that category, shown on a 0-100 scale. Higher score means fewer critical and major failures in that category.

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How to use audit history

Use Audit history as your change log:

1. Open the latest completed row.
2. Compare against the previous row.
3. Validate which fixes moved scores and which had no effect.

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LLM audit result preview: read it in this order

Start from the top and move block-by-block. This keeps the analysis focused and prevents random fixing.

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1) LLM performance and crawlability

This top block tells you if AI systems can access and process your site reliably. What to read first:

• LLM performance cards:
  • Performance
  • Accessibility
  • Best practices
  • SEO
• LLM crawlability status checks:
  • llms.txt status
  • robots.txt status
  • Sitemap status
• LLM Bots in robots.txt allow/deny table.

What to do:

• If robots.txt or sitemap is not valid, fix that first.
• If important bots are blocked, adjust rules before content improvements.
• If score cards are weak and crawlability is healthy, move to deeper content and diagnostics sections.

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2) Entity trust signals and schema validation

This section evaluates whether your site presents strong, machine-readable trust signals. What’s included:

• Entity trust signals score.
• Schema validation checks, including:
  • HowTo
  • Article
  • FAQPage
  • Organization
  • BreadcrumbList
• A recommendation list for missing or weak schema areas.

What to do:

• Add missing high-impact schema first (Organization, Article, BreadcrumbList, FAQPage where relevant).
• Keep schema aligned with actual page content.
• Use recommendation bullets as implementation tickets for dev/content teams.

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3) Content structure and semantic coverage

This section explains whether your content is easy for models to understand. What’s included:

• Content score
• Readability
• Entity keyword coverage
• Readability grade level
• Content recommendations
• NLP analysis terms and topic distribution
• Start of Initial HTML preview

How to interpret:

• Low readability with good keyword coverage means your content has topics, but clarity is weak.
• Weak entity coverage means key topics/entities are not explicit enough.
• NLP clusters show what your page is actually about from a machine perspective.

What to do:

• Simplify language in key sections.
• Improve heading hierarchy (H2/H3) and paragraph structure.
• Ensure important entities and terms appear naturally in primary sections.

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4) HTML preview, performance tabs, and diagnostics

This block helps you verify what the crawler sees and where speed is lost. What’s included:

• Initial HTML preview for source-level inspection.
• Score panel with tab views:
  • All
  • FCP
  • LCP
  • TBT
  • CLS
• Opportunities with estimated savings.
• Diagnostics with implementation details.

What to do:

• Start with the highest estimated savings items.
• Fix render-blocking CSS/JS issues first.
• Track if performance changes improve both UX and crawl efficiency.

Quick definitions:

• FCP (First Contentful Paint): time until first visible content appears.
• LCP (Largest Contentful Paint): time until the main content block appears.
• TBT (Total Blocking Time): how long scripts block user interaction.
• CLS (Cumulative Layout Shift): visual instability while the page loads.

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5) Accessibility and best-practices findings

This section highlights structural quality and implementation hygiene. What’s included:

• Accessibility checks (for example Contrast, Names and labels).
• Best practices checks:
  • Trust and Safety
  • General

What to do:

• Resolve contrast and labeling issues that block readability and machine interpretation.
• Fix recurring best-practice warnings to reduce technical fragility.
• Re-run audits after changes to confirm warning reduction.

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6) SEO, content, and crawling/indexing checks

This section is your final validation layer before closing a run. What’s included:

• SEO score and related checks.
• Content checks (title, description, and other core signals).
• Crawling and indexing checks (status/response-level validations).

What to do:

• Confirm core SEO and metadata checks pass on important pages.
• Fix crawl/index warnings before scaling content output.
• Use this section to verify technical readiness after implementation.

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If you see this, do this next

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Quick weekly checklist

1. Review list-view score direction.
2. Open latest audit details and verify crawlability first.
3. Prioritize entity/schema and content-structure gaps.
4. Fix top Lighthouse opportunities and accessibility warnings.
5. Validate SEO/content/crawling checks before marking complete.

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What to fix first

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Team routine

1. Weekly: run list review + one detailed audit deep dive.
2. Bi-weekly: validate whether shipped fixes changed detail checks.
3. Monthly: report recurring technical blockers and closure rate.

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Keep in mind

• One score alone is not enough; read section-level diagnostics.
• Not every warning has equal impact. Prioritize crawlability and trust signals first.
• High classic SEO quality does not automatically mean strong AI discoverability.

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Where to go next

• AI Search visibility
• Technical overview
• SEO audit