AI doesn’t read your generic headings: it ignores them

Do your site's section headings say "Learn more", "Find out more" or "Our services"? To AI they are empty labels: it doesn't understand what's inside and files that section in the wrong place. You're building content the system can't even read correctly. Rewriting those headings to be descriptive is a quick fix that can shift visibility immediately.

You’ve written a detailed article, full of useful answers, and you expect AI to find it and cite it. But when someone asks an AI engine something your content covers perfectly, your name doesn’t come up. The problem might not be what you write, but how you headline what you write.

Language models use the heading hierarchy as a map to understand what each section of your page is about. If that map is confusing, generic or broken, the model classifies your content under the wrong topic and discards it before even reading it in full.

Why headings are the map AI consults before anything else

When a RAG (Retrieval-Augmented Generation) system analyzes a page, it doesn’t read it from start to finish the way a human reader would. It splits the page into chunks and, to understand what each chunk is about, it looks first at the section heading. A clear, descriptive heading tells the model: “this chunk answers question X”. A generic heading like “Learn more” or “Find out more” says nothing.

In the research world, people talk about enhanced pages as a structural approach to model readability. Volpini et al. (2026) define it precisely:

“Enhanced pages transform opaque entity URIs into readable, structured information.”

Structured Linked Data as a Memory Layer for Agent-Orchestrated Retrieval

Translated into your context: a page with generic headings is opaque to AI. It can’t transform the structure into readable information because the labels are missing. It’s like a book without a table of contents, where every chapter is titled “Next Chapter”. Technically the information is there, but finding it becomes a struggle.

The practical upshot is that, when AI has to decide which content to cite in order to answer a specific question, it picks the one whose heading structure has already clearly shown it where to look.

How AI uses the hierarchy to classify your content

The H1, H2, H3 sequence isn’t decorative. To the model’s parser it works like a logical tree: the H1 declares the main topic, each H2 opens a sub-theme, each H3 details a specific aspect of the sub-theme. If you skip a level, putting an H3 under the H1 with no intervening H2, or use headings all at the same level, you’re telling the model your page has no coherent hierarchical structure.

Gao et al. (2024) document a key principle for anyone who wants to be found:

“The goal of optimizing indexing is to enhance the quality of the content being indexed.”

Retrieval-Augmented Generation for Large Language Models: A Survey

Indexing quality doesn’t depend only on how well you write, but on how well you organize what you write so the system can grasp it instantly. The heading hierarchy is the most direct tool you have for doing this.

Imagine you have a page about tax consulting services. If the structure is:

• H1: Tax Consulting for SMEs
• H2: How to reduce your tax burden with quarterly planning
• H3: Which tax breaks are active in 2026

AI immediately understands that the chunk under the H3 is about active tax breaks, in the context of quarterly planning, within a consulting service for SMEs. Three levels, three precise pieces of information, zero ambiguity.

Now imagine the same page structured like this:

• H1: Our services
• H2: Find out more
• H3: Learn more

AI reads three headings and still doesn’t know what you’re talking about. The content might be identical to the first case, but the model has no labels to classify it. When someone asks “what tax breaks are there for SMEs in 2026”, the first piece of content gets retrieved and cited, the second doesn’t.

Descriptive headings as questions: why they work better

I’ve noticed a pattern in the tests I run regularly: content with headings phrased as questions or precise statements gets cited significantly more often than content with generic titles.

The reason is mechanical. When a user asks an AI engine a question, the system looks for content that matches that query. If your heading is already phrased as a question similar to the user’s, the match is almost direct. The model doesn’t have to infer the section’s subject: it already knows it from the title.

This is consistent with the data from Volpini et al. (2026), which shows a measurable advantage for structured pages:

“Enhanced pages exposed 2.4x more discoverable links than JSON-LD pages.”

Structured Linked Data as a Memory Layer for Agent-Orchestrated Retrieval

The “2.4x” isn’t an abstract number. It means pages with a readable structure expose almost two and a half times more navigable links to the model. Headings are the first level of that structure, the point where the model decides whether it’s worth digging deeper. Every generic heading is a missed opportunity to be found.

AI doesn’t guess what’s under “Find out more”. It reads the title, finds no useful information, and moves on to your competitor’s content with a clear heading.

The signs of a broken hierarchy that makes you invisible

You don’t need a complex technical audit to tell whether your heading hierarchy has problems. There are recurring patterns you can check in a few minutes:

1. All headings as H2: Some pages use only second-level titles. To AI this is a flat list, not a structure. Without a “parent-child” relationship between the concepts, the model treats each section as independent, losing the logical thread.
2. Level jumps: Going straight from the H1 to the H3 creates a hole in the logical structure. The parser expects a gradual descent; a sudden jump makes it hard to place the chunk correctly in the topic tree.
3. Decorative headings: Titles like “Our approach” or “Our philosophy” talk about you, but say nothing about the technical content. AI can’t know that under “Our approach” sits, for example, a tax audit methodology.
4. Duplicate headings: If two sections have the same title, the model doesn’t know which one holds the relevant information and might discard both.

If you’ve read my article on how to structure self-contained sections that AI can extract as perfect units, you already know that every section must stand on its own. But a self-contained section with a generic heading is like a perfect chapter in a book without a table of contents: it exists, but nobody finds it.

How to rewrite headings so AI actually uses them

The principle is simple: every heading must declare exactly what the section below it is about. It shouldn’t be creative or evocative; it should be informative.

• Turn every heading into a question or statement: “Learn more” becomes “How the home renovation tax deduction works in 2026”. The title must contain the keywords a user would use.
• Respect the hierarchy: H1 for the main topic (only once), H2 for the sub-themes, H3 for the details. Never skip levels.
• Do the read-only test: Read only the sequence of your page’s titles. If you understand perfectly what each section is about without reading the text below, then AI will understand it too.
• One section, one concept: Keep it consistent. If a title promises “Benefits of the flat tax”, the section must not drift onto other subjects, or the chunk will be misclassified.

These checks take a few minutes, but they can drastically change how your content is perceived by AI engines. Descriptive headings are the glue that holds your content strategy together: they tell the model what to expect before it even starts reading.

Headings in the AI visibility chain

The heading hierarchy doesn’t work alone. It’s one piece that fits into a wider system. If you’ve structured your content with the answer in the first 150 tokens and self-contained sections designed for retrieval, descriptive headings are the glue that holds it all together. They tell the model what to expect in each section before it even reads it.

And if you want to give AI a complete view of your page’s structure in just a few tokens, the next step is to build a table of contents with anchor links that works as a semantic map.

Every heading you rewrite in a clear, descriptive way is one more signal the model uses to decide whether your content deserves to be cited. It’s not an opinion: it’s the mechanics of indexing.