About optimizing headlines and Sub-headlines
Headlines have long been used for SEO. For a keyword-based search engine, the H1 headline tag is the most important visual tag on the page; for a semantics-based or entity-based search engine, headlines remain critically important; for the concept of search everywhere, headlines remain critical; and, for a search engine that breaks pages into chunks, headlines and sub-headlines remain critical as structure to avoid a wall of words, with sub-headlines structurally related to their parent headlines through the H1–H6 hierarchy.
In this article, we will look at how headlines translate or transform when documents are semantically processed by AI systems, loosely corresponding to what AI models might describe as semantic weight. We will also look at how headlines define the scope of the text that follows them.
Keyword-based headlines
When the first full-page document retrieval systems appeared, they naturally used the page's headlines to help determine what the page was about. However, clever optimizers soon noticed that they could use a bait-and-switch: by putting keywords in the headlines, a page could be rewarded by the search engine even though its actual content might be weak. Keyword density became a co-factor, requiring keywords to appear throughout the page rather than relying solely on headlines or titles.
SEO debates about the importance of headlines can still be found online, but the legacy practice of using the keyword in the title and headline, along with the often-suggested practice of using it twice in the text beneath the headline, remains a useful heuristic for search-everywhere optimization. This does not mean that Google simply counts exact keyword occurrences as a ranking factor. Google's AI-based spam systems help identify bait-and-switch tactics and other techniques that are not desired in the search index.
Semantic and entity-based headlines
Semantics-based and entity-based SEO can be viewed as a transition toward AI-based search experiences. Semantically related words emerge from natural language processing, while entities, concepts, and topics are central to how large language models represent and connect information.
Essentially, search results can be produced using data created or organized by AI systems without an AI system actually generating the search results.
Functionally, the AI has created a semantic data set that distinguishes the planet Mercury from the element mercury and establishes the terms and concepts associated with each. The search engine looks at the cues provided in the query and expands the terms based on the entity the query indicates.
Sub-headlines matching the expanded set of terms associated with the entity, but not necessarily present in the search query, can register as relevant. The importance of these terms can be called semantic weight.
It can be questioned whether headline scoping is an emergent factor in a semantic and entity-based search engine, because determining the scope established by a headline requires the AI system to process the page. However, with AI Overviews, a level of AI processing is applied to pages that are served as part of the search process. For AI systems, headlines structure information, potentially helping narrow the context in which target facts are interpreted within dense content—a classic "needle in a haystack" challenge.
Legacy SEO best practices have additional competitors: pages using natural-language headlines and sub-headlines that are not keyword-stuffed or spammy, but contain legitimate content that is semantically relevant to the search. An exact-match keyword in a sub-headline is no longer required for a semantics-based search engine.
Document chunking and headline structure
A distinction needs to be made between the type of document-retrieval chunking described in academic theories of information retrieval and neural-network data chunking.
The document-retrieval theory describes an algorithm that examines a document using a sliding window, using each window of content to locate relevant content in the document for a search query.
Neural chunking is fundamentally different. By using natural breakpoints in the content, structural sections can be processed as semantic units or nodes; the AI does not process the document as a wall of words.
Headline scoping can help mitigate the "needle in a haystack" problem created by a wall of words. It provides a structural breakpoint that helps establish the scope of the content that follows. Without mechanisms for structuring or limiting the context being processed, the computational cost of processing long sequences increases. The difficulty of maintaining accuracy as the amount of text accumulates has also been called "context rot."
Research into structure-aware semantic chunking has also found that preserving document structure and heading context can affect retrieval performance.
Anthropomorphizing the data retrieval process
It should be pointed out that we cannot micromanage how an AI processes a webpage. However, we can improve our understanding by considering how a human reads content and scopes information by scanning the headlines of a document to locate relevant facts and information.
This is an analogy, not a claim that AI systems read webpages the same way humans do. The value of the analogy is that it provides an intuitive way to understand why headlines can establish scope and help separate one subject or concept from another.
AI systems achieve similar functional outcomes through different means. Although we may not fully understand the details of how the human brain accomplishes these processes, AI systems can similarly scope and assign differing importance to content based on its context and structure. Clear, natural-language sub-headlines create explicit semantic boundaries that can help RAG pipelines and AI systems organize documents into focused, verifiable passages.
For both humans and AI systems, a sub-headline provides context for the content beneath it by relating that content to the subject established by its parent headline. The H1–H2–H3 hierarchy therefore creates a nested structure in which each sub-headline narrows or qualifies the scope established by its parent, helping make the relationship between the subject and the supporting information explicit.
Consider the sub-headline '''Mercury's orbit'''. This unambiguously establishes that the content is about the planet Mercury rather than the element mercury. The text under the headline might read: "It is the closest planet to the Sun and can appear as a 'morning star' because it is sometimes visible from Earth in the morning before sunrise. It takes approximately 88 Earth days to orbit the Sun."
AI systems can resolve references such as "it" from contextual information and associate the facts in the passage with the planet rather than the element.
The headline establishes the subject and scope of the following text. In this example, the headline provides additional structural context for interpreting what the following statements are about. The information is therefore not simply a wall of words; the headline establishes a boundary that helps identify the subject of the passage.
AI-aware Search Everywhere headline usage
Entity- or topic-based keywords are not themselves problematic for AI systems, and keywords continue to provide legacy support for traditional search systems. The proper SEO best-practice H1–H2–H3 hierarchy should be maintained. Semantic headlines based on long-tail searches can still be used, but AI systems can interpret the semantic relationships between the words rather than relying solely on exact keyword matching.
Headlines and sub-headlines are important because they help AI systems locate, interpret, mention, or cite information from a page. An AI system may overlook relevant information buried in an unstructured wall of content—the "needle in a haystack" problem. Be generous with structural cues that organize information for both humans and AI.
Published
by Wayne Smith – Raising the Standards
Wayne Smith has worked in online marketing, search, and web development for several decades. His work includes building document retrieval systems, search engine simulations, and AI-assisted information systems. Drawing from software testing, information retrieval, and reverse engineering, he studies how AI systems discover, interpret, and synthesize information into answers.
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