AI-based discovery offers a new level of sophistication in surfacing content, without relying solely on keywords. Beyond keyword-string-first approaches, contextual and semantic elements are now more important than ever.

Optimization is no longer about just reinforcing the keyword. It’s also about constructing a retrievable semantic environment around it.

This impacts how we write, create, and think about content. It applies whether you write every word yourself or employ automated workflows.

Reframing your publishing strategy around context

Much has already been written about the concepts covered here. This discussion focuses on tying them together into a more cohesive publishing strategy and tactical approach.

If you’re already operating in a context mindset, you’re likely making these elements work for you. If you’re still using keyphrase-first approaches and want a stronger grasp of deeper contextual and semantic strategy, keep reading.

Context, semantics, meaning, and intent have long been core to optimization. What’s changed is how content is presented and discovered, particularly within LLM-based platforms.

This shift affects how context is categorized and structured across a website. It applies to site taxonomy, schema, internal linking, and content chunking and clustering.

It also means moving away from verbose word counts and getting to the point. That benefits both the machine layer and the human reader.

Keywords aren’t obsolete. But they don’t function as isolated optimization tactics. Context-led strategies aren’t new. However, they require greater attention to define what your publishing strategy means moving forward.

Dig deeper: If SEO is rocket science, AI SEO is astrophysics

Structure for a contextual-density approach

When considering the keyphrase as a multidimensional point for building semantics, it may be more productive to think of these combined concepts within a single framework. In essence, every topic exists as a semantic field rather than a word or phrase. These areas include:

• Axis term (primary topic/keyphrase).
• Structural context (secondary and tertiary concepts).
• Problem context (intent).
• Linguistic variants (stemmed or fanned phrasing).
• Entity associations.
• Retrieval units (chunk-level readability).
• Structural signals (internal links, schema, and taxonomy).

While the main keyphrase is the anchor and axis point for the linguistic dimensions that surround it, almost everything else defines true performance and meaning apart from the keyword.

In other words, the sum of all the “other” words — headings, subheadings, references to related concepts, and various entities related to the keyphrase — is just as important as the keyphrase itself. This is a very basic concept in producing well-thought-out writing, but it’s now more important.

Context density and SERP-level linguistic analysis

One way to think about this shift is by comparing keyword-level linguistic analysis with search engine results page-level linguistic analysis.

SERP-level linguistic analysis isn’t new. One of the first major tools to address this concept was Content Experience by Searchmetrics and Marcus Tober.

The platform launched around 2016 — priced for enterprises — and focused on scraping the top results page for a given keyword, then averaging and weighting the other words common across high-ranking pages.

The idea was that those additional words and entities, which helped define a comprehensive set of results for a topic, would yield key semantic indicators for content performance.

These reports provided stemmed concepts, entities, and specific language modifiers to add hyper-context to the main topic.

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

In my experience, these types of analyses have been very useful for creating high-performing content.

They’ve worked well competitively and have been especially effective in linguistic areas where competitors lacked this level of analysis in their own content.

Dig deeper: Content scoring tools work, but only for the first gate in Google’s pipeline

Using secondary and tertiary keyphrases as contextual linguistic struts

Understanding this type of analysis helps you delve deeper into semantic page construction by categorizing and emphasizing ancillary language into a hierarchy, particularly in second- and third-tier levels. You can go as deep with the hierarchy as your content scope permits.

Secondary and tertiary keywords should form what I often refer to as “linguistic struts” — supporting elements that reinforce your main topic while expanding its scope and relevance.

Think of them as context stabilizers or intent differentiators for a given 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 architecture, whether it’s introducing a new subtopic, answering a related question, or providing additional context for your primary theme.

Once you’ve defined this secondary and tertiary language, it can guide your outline and then the final writing. 

This approach applies to everything from manually written work to fully automated and synthetic processes.

Stemmed linguistics

One of the most powerful aspects of comprehensive contextual keyword optimization is its ability to capture stemmed and fanned-out searches — related queries that share common roots or concepts with your optimized keywords.

In other words, related keyphrases and searches you may not have directly optimized for within the primary topic. These types of searches can be extremely valuable, often more so than the primary keyphrase, because they reflect more refined and deliberate intent.

For example, if you’ve created a comprehensive guide for “content marketing,” your page might also rank for searches such as “implementing content marketing strategies,” “content marketing strategy implementation,” or “hire B2B content marketing expert.”

The sum of these stemmed variations often represents significantly higher-intent search volume than any individual keyword.

The more thoroughly you cover secondary and tertiary keywords, the more stemmed and fanned searches you’re likely to capture.

Dig deeper: How to use relationships to level up your SEO

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High-level technical foundations for contextual emphasis

When discussing the move from a string-based strategy to a context-based strategy, it’s as much about how machines process content as it is about writing.

LLM-powered platforms evaluate context at multiple layers — how content is segmented, how topics are structurally connected, and how meaning is formally implied.

Retrieval mechanics: From pages to chunks

Large language models retrieve segments of content — referred to as “chunks” — that have been transformed into vector representations.

In simplified terms, your page is broken into retrievable units. Those units are evaluated for contextual similarity to a prompt, and the LLM selects the chunks that best align with the intent and semantic patterns in the query.

Contextual similarity emerges from co-occurring terms, related entities, problem points, and semantic density within a chunk.

If a chunk lacks contextual depth — in other words, if it simply repeats a primary term without expanding the surrounding semantic field — it becomes thin in the embedding layer.

Thin chunks are less likely to be retrieved, even if the page ranks well in traditional search.

The implication for your writing is straightforward: Getting to the point faster can be a significant advantage at both the page and site levels. It can improve machine readability and create a better human reading experience, serving multiple KPIs.

Dig deeper: Chunk, cite, clarify, build: A content framework for AI search

Structural context: Architecture as meaning

How your content is organized structurally also infers meaning within LLM-based discovery. Beyond providing a taxonomical hierarchy, structure acts as a contextual signal.

Architecture teaches the system how your topics relate to one another. Internal links apply inference and meaning to related topics and entities.

Taxonomy infers the semantic mapping of your connected content within a domain or across domains. URL naming and structure further signal hierarchy and topical relationships.

When a page sits within a clearly defined topical cluster and links to related concepts and subtopics, it inherits contextual reinforcement.

An LLM understands what the page says and where it lives conceptually within your broader domain.

Schema and entity context

There’s also a layer of meaning that can be formally stated through schema markup.

Schema markup and entity modeling provide explicit clarification of what something is, who is involved, and how elements relate to one another.

Where linguistic context builds meaning implicitly through unstructured writing, schema states its intended meaning through structured data.

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

This doesn’t replace strong writing, but it strengthens it by ensuring machine-readable contextual emphasis.

In a contextual discovery environment, every technical element exists to strengthen semantic retrievability.

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

Dig deeper: Organizing content for AI search: A 3-level framework

Moving to a context-first strategy

When you align linguistics, structure, and declaration around a clear topical axis, the strategy centers on the contextual environment.

Transitioning from a purely keyphrase-centered strategy may seem daunting at first, but it’s something you can begin doing today in how you write and research your content.

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