LLM Optimization Checklist: 8 Things to Check Before You Publish

Most content teams are optimizing for Google. Fewer are checking whether their content will be picked up by ChatGPT, Perplexity, Claude, or Gemini. This checklist closes that gap.

Run each new piece of content through this list before publishing. Run your existing high-priority pages through it as an audit. Both will surface improvements worth making.

1. Does This Content Directly Answer a Question an AI User Would Ask?

The check: Write out the natural-language question that someone would type into ChatGPT to get information on this topic. Does your content directly and thoroughly answer that question?

Pass: The content answers the question in the first two to three paragraphs, then provides depth and supporting detail throughout.

Fail: The content circles the topic without directly answering the core question, or buries the answer in the fifth paragraph after a long introduction.

Why it matters: AI models are answering questions. Content that directly answers questions is more extractable than content that provides general coverage of a topic area. The model needs to match your content to the user's query, and that match is stronger when your content answers the question explicitly.

Fix: Add a direct answer to the core question in the opening paragraph. Structure the rest of the content as support for that answer.

2. Is the Entity Clear?

The check: Read the first paragraph of your page. Can you identify the primary entity (brand, product, concept) the page is about? Is that entity's name, description, and context stated explicitly?

Pass: The primary entity is named and described in the first paragraph with enough context that an AI model encountering this page for the first time would know what it is.

Fail: The page assumes context that readers (or AI models) may not have. Entity name may appear without description, or description appears without clear entity identification.

Why it matters: AI models build entity representations. If your content does not clearly identify and describe the entity it is about, the model cannot reliably associate that content with the entity in its answers.

Fix: Add a clear entity identification sentence to your introduction: "Bingly is a brand monitoring and AI visibility platform for SEO professionals and marketers." Simple, specific, attributable.

3. Are There Specific, Citable Claims?

The check: Count the specific, factual claims in your content. Data points, percentages, named use cases, concrete examples. Aim for at least two to three per major section.

Pass: Multiple specific claims that a model could extract and attribute to your page or brand.

Fail: Mostly abstract value propositions, vague benefits language, or claims that are either obvious ("helps teams collaborate") or unverifiable ("the best solution in the market").

Why it matters: AI models extract and cite specific information. Vague content cannot be cited. Specific content can. This is the single biggest gap between content that earns AI citations and content that does not.

Fix: For each major section, add at least one specific claim. If you do not have data, add a named use case ("For a SaaS company selling to mid-market buyers, this means...") or a concrete scenario ("When an engineering team is evaluating CI/CD tools, the first question they ask is...").

4. Is the Heading Structure Machine-Readable?

The check: Read only the headings of your content. Can you understand the structure and content of the page from headings alone? Is the heading hierarchy logical (H1 > H2 > H3)?

Pass: Headings tell the story of the content. H2s represent major sections; H3s represent sub-points within those sections.

Fail: Headings are vague, inconsistent, or used for visual formatting rather than structural meaning.

Why it matters: AI models parse content structure using headings. A clear heading hierarchy helps models identify and extract the relevant sections of your content for a given query. It also makes your content useful for featured snippets and PAA boxes in traditional search.

Fix: Rewrite headings to be descriptive and specific. "The Benefits" becomes "Why Teams Switch from Manual Tracking to Automated AI Visibility Monitoring." Specific headings create extractable sections.

5. Do You Have Schema Markup?

The check: Use Google's Rich Results Test on the page. Are there relevant schema types implemented?

Pass: Appropriate schema types are present (Article for blog posts, FAQPage for Q&A content, Organization on your homepage, Product on product pages).

Fail: No schema markup, or only generic metadata without structured data.

Why it matters: Schema markup is explicit machine-readable metadata. It tells retrieval systems and AI models what type of content this is, what entity it is about, and what it contains. Pages with schema markup are more reliably parsed and categorized. See Schema Markup for AI Search for implementation specifics.

Fix: Add at minimum Article schema for blog posts and Organization schema for your homepage. Add FAQPage schema for any content with Q&A structure.

6. Is the Page Topically Focused?

The check: Describe the page's topic in one sentence. Now check whether the content stays on that topic or wanders into adjacent topics without clear connection.

Pass: The page has a clear, focused topic and all content serves that topic. Adjacent topics are acknowledged briefly and linked to other resources.

Fail: The page tries to cover too many topics at once, creating a shallow treatment of multiple subjects rather than a deep treatment of one.

Why it matters: AI models associate sources with topics through their topical depth. A page that is clearly about one specific thing is more strongly associated with that topic than a page that covers many things superficially.

Fix: Split unfocused content into multiple focused pieces. Keep each page tightly scoped to one main question or topic. Link between related pieces rather than cramming everything into one.

7. Is Your Brand's Third-Party Presence Current?

The check: Search your brand name on G2, Capterra, Product Hunt, LinkedIn, and your top two or three industry publications. Is the information accurate and current?

Pass: Your profiles are accurate, recently updated, and present on the major platforms relevant to your category.

Fail: Your profiles are outdated, incomplete, or describe an older version of your product.

Why it matters: AI models do not only cite your own domain. Third-party sources contribute to a model's entity representation of your brand. Outdated third-party information is a source of AI inaccuracy that your own website cannot fix.

Fix: Review and update your major third-party profiles quarterly. This takes 30 minutes and has outsized impact on AI citation accuracy.

8. Have You Checked How AI Models Describe You After Publishing?

The check: After publishing (or updating) this content, query ChatGPT, Perplexity, and Claude with the target question. Does your brand appear? If so, how are you described?

Pass: Your brand appears in at least one major AI model's response for the target query, and the description is accurate.

Fail: You do not appear, or you appear with inaccurate characterization.

Why it matters: Publishing and measuring closes the optimization loop. Without this check, you are producing content blind. Knowing that a piece of content earned (or did not earn) an AI citation is the feedback signal that drives continuous improvement.

Fix: Add a 30-day post-publish AI citation check to your content calendar. Use Bingly's tracking to automate this across all major models rather than checking manually each time.

Using the Checklist

Run through these eight points before publishing any content you want to earn AI citations. For existing content, prioritize your ten most important pages and run the audit sequentially.

The highest-leverage items for most teams are criteria 3 (specific claims), 4 (heading structure), and 8 (post-publish check). These are the most commonly missed and have the most direct impact on AI citation rates.

Track your LLM optimization results across every major AI model with Bingly.