Using Web Audit to Detect LLM Accessibility Issues

Intro

Traditional SEO audits look for crawlability issues, broken links, missing metadata, and on-page errors. But in 2025, technical SEO is only half the picture.

Modern visibility depends on a new requirement:

LLM accessibility — how easily AI systems can parse, chunk, embed, and interpret your content.

AI search engines such as:

• Google AI Overviews

• ChatGPT Search

• Perplexity

• Gemini

• Copilot

do not evaluate pages the way Googlebot does. They evaluate:

• structural clarity

• chunk boundaries

• embedding quality

• semantic coherence

• entity stability

• schema richness

• machine readability

If your site is technically correct but not LLM-accessible, you lose:

• generative citations

• AI Overviews inclusion

• semantic retrieval ranking

• entity graph visibility

• conversational relevance

The Web Audit tool allows you to detect these issues systematically — long before LLMs downrank or ignore your content.

This guide explains exactly how to use Web Audit to uncover LLM accessibility problems, why they matter, and how to fix them.

1. What Are LLM Accessibility Issues?

LLM accessibility = how easily AI systems can:

• ✔ crawl your content

• ✔ interpret your structure

• ✔ chunk your sections

• ✔ embed your meaning

• ✔ identify your entities

• ✔ align you with the knowledge graph

• ✔ retrieve your content accurately

LLM accessibility issues are not limited to:

• broken HTML

• poor Lighthouse scores

• missing meta tags

Instead, they arise from:

• structural ambiguity

• inconsistent headings

• broken schema

• mixed topic chunks

• poor semantic segmentation

• machine-hostile formatting

• outdated entity definitions

• missing canonical meaning

• inconsistent metadata

The Web Audit tool detects many of these implicitly through standard SEO checks — but now they also map directly to LLM-first problems.

2. How Web Audit Maps to LLM Accessibility

Web Audit checks dozens of elements. Here’s how each category connects to LLM issues.

1. Crawlability Issues → LLM Ingest Failure

If your pages cannot be fetched by crawlers, LLMs cannot:

• re-embed

• update vectors

• refresh meaning

• fix outdated interpretations

Web Audit flags:

• robots.txt blocks

• canonicalization errors

• inaccessible URLs

• redirect loops

• 4xx/5xx errors

These directly cause stale or missing embeddings.

2. Content Structure Issues → Chunking Failures

LLMs segment content into chunks using:

• H2/H3 hierarchy

• paragraphs

• lists

• semantic boundaries

Web Audit identifies:

• missing headings

• duplicated H1

• broken hierarchy

• overly long blocks

• meaningless headings

These issues create noisy embeddings, where chunks contain mixed topics.

3. Schema Errors → Entity Ambiguity

Schema isn’t for Google anymore — it is now an LLM comprehension layer.

Web Audit detects:

• missing JSON-LD

• conflicting schema types

• invalid properties

• schema not matching page content

• incomplete entity declarations

These cause:

• entity instability

• knowledge graph exclusion

• poor retrieval scoring

• misattributed content

4. Metadata Problems → Weak Semantic Anchors

Web Audit flags:

• missing meta descriptions

• duplicate titles

• vague title tags

• absent canonical URLs

These impact:

• embedding context

• semantic anchor quality

• chunk meaning precision

• entity alignment

Metadata is LLM scaffolding.

5. Duplicate Content → Embedding Noise

Web Audit detects:

• content duplication

• boilerplate repetition

• near-duplicate URLs

• canonical conflicts

Duplicate content produces:

• conflicting embeddings

• diluted meaning

• low-quality vector clusters

• decreased retrieval confidence

LLMs downweight redundant signals.

6. Internal Linking Issues → Weak Semantic Graph

Web Audit reports:

• broken internal links

• orphan pages

• thin cluster connectivity

Internal linking is how LLMs infer:

• concept relationships

• topical clusters

• entity mapping

• semantic hierarchy

A poor internal graph = poor LLM understanding.

7. Page Speed Issues → Crawl Frequency & Re-Embedding Delay

Slow pages reduce:

• recency updates

• crawling frequency

• embedding refresh cycles

Web Audit flags:

• render-blocking resources

• oversized JavaScript

• slow response times

Poor performance = stale embeddings.

3. The Web Audit Sections That Matter Most for LLM Interpretation

Not all audit categories are equally important for LLM accessibility. These are the critical ones.

1. HTML Structure

Key checks:

• heading hierarchy

• nested tags

• semantic HTML

• missing sections

LLMs need a predictable scaffold.

2. Structured Data

Key checks:

• JSON-LD errors

• invalid schema

• missing/incorrect attributes

• missing Organization, Article, Product, Person schema

Structured data = meaning reinforcement.

3. Content Length & Segmentation

Key checks:

• long paragraphs

• content density

• inconsistent spacing

LLMs prefer chunkable content — 200–400 tokens per logical block.

4. Internal Linking & Hierarchy

Key checks:

• broken internal links

• orphaned pages

• missing breadcrumb structure

• inconsistent siloing

Internal structure influences semantic graph alignment inside vector indexes.

5. Mobile & Performance

LLMs rely on crawlability.

Performance issues often prevent full ingestion.

4. Using Web Audit to Diagnose LLM Accessibility Problems

Here is the workflow.

Step 1 — Run a Full Web Audit Scan

Start with the highest-level view:

• critical errors

• warnings

• recommendations

But interpret each through the lens of LLM comprehension.

Step 2 — Examine Schema Issues First

Ask:

• Are your entity definitions correct?

• Is Article schema present on editorial pages?

• Does Person schema match the author name?

• Are Product entities consistent across pages?

Schema is the #1 LLM accessibility layer.

Step 3 — Review Content Structure Flags

Look for:

• missing H2s

• broken H3 hierarchy

• duplicate H1

• headings used for styling

• giant paragraphs

These directly break chunking.

Step 4 — Check for Duplicate Content

Duplicates degrade:

• embeddings

• retrieval ranking

• semantic interpretation

Web Audit’s duplication report reveals:

• weak clusters

• content cannibalization

• meaning conflicts

Fix these first.

Step 5 — Crawlability & Canonical Issues

If:

• Google can’t crawl

• ChatGPT can’t fetch

• Perplexity can’t embed

• Gemini can’t classify

…you’re invisible.

Fix:

• broken pages

• incorrect canonical tags

• redirect failures

• inconsistent URL parameters

Step 6 — Review Metadata Uniformity

Titles and descriptions must:

• match the page

• reinforce the primary entity

• stabilize meaning

Metadata is the embedding anchor.

Step 7 — Check Internal Linking for Semantic Alignment

Internal links should:

• connect clusters

• reinforce entity relationships

• provide context

• build topic maps

Web Audit highlights structural gaps that break LLM graph inference.

5. The Most Common LLM Accessibility Issues Web Audit Reveals

These are the real killers.

1. Missing or Incorrect Schema

LLMs cannot infer entities. Results: poor citations, misrepresentation.

2. Unstructured Long Blocks of Text

Models cannot chunk cleanly. Results: noisy embeddings.

3. Weak or Conflicting Metadata

Titles/descriptions don’t define the meaning. Results: ambiguous vectors.

4. Duplicate Content

LLMs see conflicting meaning clusters. Results: low trust.

5. Poor Heading Hygiene

H2/H3 structure is unclear. Results: poor chunk boundaries.

6. Orphan Pages

Pages floating without context. Results: no semantic graph integration.

7. Slow Performance

Delays re-crawling and re-embedding. Results: stale meaning.

6. How to Fix LLM Accessibility Issues Using Web Audit Insights

A clear action plan:

Fix 1 — Add Article, FAQPage, Organization, Product, and Person Schema

These stabilize entities and meaning.

Fix 2 — Rebuild H2/H3 Hierarchies

One concept per H2. One sub-concept per H3.

Fix 3 — Rewrite Long Paragraphs Into Chunkable Segments

2–4 sentences max.

Fix 4 — Clean Your Metadata

Make every title definitional and consistent.

Fix 5 — Consolidate Duplicate Pages

Merge cannibalized content into single, authoritative clusters.

Fix 6 — Build Internal Clusters With Strong Linking

Improve:

• entity reinforcement

• topical clusters

• semantic graph structure

Fix 7 — Improve Performance and Caching

Enable:

• fast loads

• efficient crawlability

• rapid embedding updates

Final Thought:

Web Audit Isn’t Just Technical SEO — It’s Your LLM Visibility Diagnostic

Every LLM accessibility issue is a visibility issue.

If your site is:

• structurally clean

• semantically organized

• entity-accurate

• schema-rich

• chunkable

• fast

• consistent

• machine-readable

…AI systems trust you.

If not?

You disappear from generative answers — even if your SEO is perfect.

Web Audit is the new foundation for LLM optimization because it detects everything that breaks:

• embeddings

• chunking

• retrieval

• citation

• knowledge graph inclusion

• AI Overviews visibility

Fixing these issues prepares your site not just for Google — but for the entire AI-first discovery ecosystem.