How to build an AI-first search optimization strategy

AI-based discovery provides a new level of sophistication in content discovery, without relying solely on keywords. Besides the original keyword methods, contextual and semantic elements are now more important than ever.

Optimization is no longer about keyword optimization. It’s about building a reversible semantic space around it.

This affects the way we write, create, and think about content. It works whether you type each word yourself or use an automated workflow.

Reengineering your publishing strategy around context

Much has been written about the ideas discussed here. This discussion focuses on bringing them together into a cohesive publishing strategy and creative approach.

If you already work in contextual thinking, you probably have these features working for you. If you’re still using keyword phrase basics and want a solid grasp of deep contextual and semantic strategy, read on.

Content, semantics, meaning, and purpose have long been at the core of development. What has changed is how content is presented and how content is found, especially within LLM-based platforms.

This change affects the way content is divided and structured throughout the website. It works on site taxonomy, schema, internal linking, and content integration and integration.

It also means moving away from counting verbs and getting to the point. That benefits both the machine layer and the human reader.

Keywords are no longer valid. But they don’t work as different development strategies. Content-led strategies are not new. However, they need a lot of attention to define what your publishing strategy means going forward.

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Structure of the context density method

If you consider a keyword phrase as a multi-dimensional building block of semantics, it may be more productive to think of these combined concepts within a single framework. In fact, every subject exists as a semantic field rather than a word or phrase. These areas include:

• Axis term (main title/key phrase).
• Total structure (secondary and higher level concepts).
• The problem is context (purpose).
• Variations of language (sentences with a title or subject).
• Business organizations.
• Retrieving units (chunk level reading).
• Structural features (internal links, schema, and taxonomy).

While the main keyword phrase is the anchor and axis point of the language dimension around it, almost everything else defines the true function and meaning without the keyword.

In other words, the sum of all the “other” words – titles, subheadings, references to related concepts, and various associations associated with the keyword – are just as important as the keyword itself. This is a very basic concept in producing well-thought-out writing, but now it is very important.

Content density and language analysis for SERP ranking

Another way to think about this change is to compare keyword-level language analysis with search engine results for page-level language analysis.

Language analysis for SERP ranking is not new. One of the first major tools to address this concept was Content Experience by Searchmetrics and Marcus Tober.

The platform was launched around 2016 – it’s priced for businesses – and focuses on uncovering the top results page for a given keyword, then averaging and ranking other common words on all the top pages.

The idea was that those additional words and associations, which helped define a broader set of subject outcomes, would reveal the main semantic indicators of content performance.

These reports provided root concepts, entities, and specific language modifiers to add greater context to a larger topic.

Other tools, such as Clearscope, use different methods to achieve similar results.

In my experience, these types of analysis have been very helpful in creating highly effective content.

They have worked well against the competition and have been particularly successful in language areas where competitors do not have this level of analysis in their content.

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Using secondary and tertiary keywords as keywords of the situation

Understanding this type of analysis helps you go deeper into semantic page design by breaking down and highlighting more language in the program, especially at the second and third level. You can drill down by category for your favorite content.

Secondary and tertiary keywords should form what I often call “linguistic struts” – supporting elements that strengthen your main topic while expanding its scope and relevance.

Think of them as focal points or objective dividers for a particular topic or theme. The choices you make here ultimately define the context and relevance of your content.

Each secondary keyword should serve a specific purpose within your page structure, whether it’s introducing a new subheading, answering a related question, or providing more context for your main theme.

Once you have defined this second and higher language, it can guide your draft and final writing.

This approach applies to everything from manual work to fully automated and automated processes.

Stemmed linguistics

One of the most powerful features of contextual keyword optimization is its ability to capture root and secondary searches – related queries that share the same roots or concepts as your optimized keywords.

In other words, search-related keywords that you may not have optimized specifically within the main article. These types of searches can be extremely valuable, often more so than the primary keyword phrase, because they reflect a more refined and deliberate intent.

For example, if you created a comprehensive guide to “content marketing,” your page might also rank for searches like “implementing content marketing strategies,” “implementing a content marketing strategy,” or “hiring a B2B content marketing expert.”

The sum of these rooted variables usually represents a much higher objective search volume than any individual keyword.

If you carefully combine secondary and tertiary keywords, the more branded and fan-made searches you’re likely to capture.

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Advanced technical foundations for context emphasis

When discussing moving from a thread-based strategy to a context-based strategy, it is as much about how machines process content as it is about writing.

Powerful LLM platforms examine context at multiple levels – how content is categorized, how topics are linked structurally, and how meaning is formally defined.

Mechanical recovery: From pages to fragments

Large language models return pieces of content – called “chunks” – that have been converted into vector representations.

In simple words, your page is divided into retrievable units. Those units are checked for similarity of context and information, and LLM selects the units that best match the purpose and semantic patterns in the question.

Content similarity comes from co-occurring words, related entities, problem points, and semantic density within a passage.

If the passage lacks contextual depth – in other words, if it repeats the main term without expanding the surrounding semantic field – it is thin in the embedding layer.

Thin slices are less likely to be returned, even if the page ranks well in general search.

The implication of your writing is straightforward: Getting ranked quickly can be a huge benefit for both page and site rankings. It can improve machine readability and create a better human learning experience, providing multiple KPIs.

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The essence of structure: Structure as meaning

How your content is structured and conveys meaning within LLM-based discovery. Beyond providing a taxonomical system, structure serves as a context signal.

Architecture teaches the system how your subjects relate to each other. Internal links apply concepts and definitions to related topics and organizations.

Taxonomy includes a semantic map of your linked content within a domain or across domains. URL composition and structure further signal ranking and topic relationships.

If a page stays within a set of clearly defined topics and links to related concepts in the subtopics, it inherits contextual reinforcement.

LLM understands what a page is and where it sits conceptually within your wider domain.

Schema and business context

There is also a layer of meaning that can be formally expressed through schema tags.

Schema markup and business modeling provide a clear description of what something is, who is involved, and how the elements relate to each other.

Where the context constructs meaning implicitly through unstructured notation, schema expresses its intended meaning through structured data.

In doing so, it formalizes business relationships, reduces ambiguity, and reinforces identity and title signals across platforms.

This does not replace strong writing, but strengthens it by ensuring emphasis on machine-readable content.

In the area of ​​context discovery, every technology exists to enhance semantic discovery.

For a deep dive into the technological changes in content discovery in the age of AI, I recommend Duane Forrester’s book, “The Machine Layer.”

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Moving on to the first context strategy

If you align language, structure, and pronunciation in a clear topic area, the strategy focuses on the context area.

Switching from a keyword-focused strategy may seem difficult at first, but it’s something you can start doing today in the way you write and research your content.

In simple terms, going for a context-first strategy is about how you go about writing at both the page and site levels and making your content as machine-readable as possible.