Online search is in the middle of a context revolution. 

What do we mean?

Before LLM-powered search (ChatGPT, Google’s AI Overviews, etc.), algorithms were ultimately reliant on keyword matching to rank online content. 

While Google’s algorithm has steadily incorporated semantic elements through updates like Hummingbird, RankBrain, BERT, and MUM, lexical keyword matching still lies at the core of its ranking logic. 

That’s why classic search engines like Google require ‘search engine speak,’ where you string together a series of keywords instead of using natural language. 

For example, users have become accustomed to typing phrases like ‘best pizza st. petersburg’ into engines like Google and Bing. 

With AI search tools, users can search for things using complete sentences (‘what’s the best pizza place in St. Petersburg?’). 

Moreover, LLMs match meanings instead of exact words. 

To a basic keyword-matching algorithm, words are just words. To an LLM, words carry meaning, entities are identified (people, organizations, places, etc.), and context is understood. 

This is why we say the search world is in the middle of a contextual relevance revolution. Semantic retrieval models give AI models the ability to interpret meaning and intent the way humans do, which is why marketers must change their SEO strategies to keep up. 

In this post, we’ll break down how you can optimize for the new contextual search model. 

Keyword Matching: The Old Search Model

For more than 25 years, the classic SEO formula went like this: sprinkle relevant search terms throughout your content in key areas, and hope that search engines noticed. 

Of course, it became more complicated than that as years went on, but keyword research and optimization is the literal foundation of organic SEO. 

Rather than bore you with a history lesson, let’s examine the limitations of the keyword model. This will help you appreciate the advancements being made with semantic search and contextual relevance. 

Here are the main drawbacks to the keyword matching model:

• Context blind – Keyword-based algorithms aren’t able to infer the relationships between words and often rely on exact-match or partial-match keywords in order to rank content. For example, the phrase “best practices for fitness accessories’ might not rank for the keyword ‘fitness accessories for beginners’ unless the exact phrasing is used, even though the two are contextually related. 

• Ambiguity – Keyword matchers can’t recognize entities (organizations, brands, people, etc.), so words with dual meanings (apple the fruit and Apple the company) can lead to confusion and irrelevant results. This is because keywords are just static strings of text with no meaning behind them. 

• Causes keyword stuffing – Despite Google’s best efforts, scores of marketers approach SEO by stuffing their content with as many exact-match keywords as possible. This creates unnatural content that feels forced and is difficult for users to read. 

• Inability to process long-tail, natural language queries – If you weren’t fluent in ‘search engine speak,’ you’d have a hard time finding what you were looking for on Google, especially if the concept was complicated or required lots of words. 

• User experience is often an afterthought – Google wants websites to produce helpful content because that’s what’s best for its users. However, due to lexical keyword matching and link counting, it was still possible for many years to see results using practices like keyword stuffing and link farming. Because of this, Google’s search results were often cluttered with spammy, unhelpful content. 

As you can see, there were many flaws with the keyword-matching system. Now, let’s analyze how the transition to LLM-based search solved all these issues. 

Contextual Relevance: The New Search Model

While LLMs don’t crawl and index the internet, they are able to pull online content through methods like web scrapers, APIs, and plugins. 

Gemini is the exception, since it has direct access to Google’s massive search index. Other platforms, like ChatGPT, access online search indexes (like Google’s, but also others like Bing) through indirect methods. 

Since they have real-time access to the internet, LLM-powered assistants are able to act as proxies for major search engines, and they’re everywhere now. 

Even Google’s own AI Overviews dominate the results pages, driving CTRs down across the board as a result. 

However, there’s a pretty good reason why AI search tools are so prominent now. 

They work better. 

Instead of layering semantic enhancements onto keyword-matching systems, LLMs were built from the ground up with contextual understanding in mind. 

Through natural language processing and entity recognition, LLMs can grasp the meaning behind strings of keywords. They understand the relationships between words and can resolve ambiguity automatically.    

As a result, you can talk to an AI search assistant just as you would to another human. This negates the need for ‘search engine speak’ and enables users to ask detailed, long-form questions. 

Since context matters more than mathematical metrics like keyword frequency and link volume, AI search models are better at finding online content that truly matches the user’s intent. 

Tricks like keyword spamming and artificially inflating domain authority through volume don’t work. 

That means the content that gets cited must be truly helpful, relevant, and trustworthy. 

How AI search platforms pull online content

Here are the primary components that make AI search tick:

• Natural language processing (NLP) – AI search tools can understand language well enough that keyword-rich shorthand is no longer necessary. 

• Entity recognition and understanding – LLMs identify entities (like your brand), link them to knowledge base entries (like Wikidata), and connect them to other relevant entities (like your niche). 

• Brand mentions, reviews, and brand sentiment – To weigh trust, AI search tools don’t consider third-party metrics like Domain Authority or Domain Rating. Instead, they analyze a brand’s web mentions (linked and unlinked), reviews, and brand sentiment from community members. 

• Structured data – The presence of structured data, namely semantic HTML and schema markup, makes online content easy for LLMs to parse and removes all ambiguity. They’re effectively special labels for aspects of your content like recipes, reviews, and authors. 

These factors all contribute to how LLMs understand, parse, and cite content. 

Here’s a breakdown of the difference between the keyword model and the contextual model:

How to Optimize Your Content for Context Instead of Keywords

For marketers, the transition to context over keywords necessitates a change in SEO strategy. This can be a daunting task because the fundamentals of SEO have remained the same for so long. 

Making a successful jump to generative engine optimization (GEO) may require unlearning some habits that you’ve carried for a very long time, like going out of your way to include exact-match keywords in your content. 

Since LLMs can understand the relationship between concepts, using exact keyword phrasing is no longer necessary. 

Let’s consider an example. 

In the old keyword model, if a user searched for ‘how to build backlinks’, the algorithm would look for pages containing those exact words, like:

• How to Build Backlinks in 2025

• • Articles that contain the words ‘how to build backlinks’ in the copy 

• Learning How to Build Backlinks 

If your content didn’t contain these phrases, Google likely wouldn’t rank it for the keyword, even if your guide contained the absolute best advice for building backlinks. 

The new model solves this issue. If a user searched for the same prompt on a tool like ChatGPT (‘how to build backlinks’), it could pull content like this:

• How to Improve Your Website’s Authority 

• Top SEO Tips for 2025 

• How to Get Other Sites to Reference You 

As you can see, none of these titles has the exact phrase ‘how to build backlinks’ in them, but they’re all fair game for ChatGPT citations. 

Why?

AI models know that backlinks are conceptually linked to things like website authority, references, and SEO tips. Furthermore, LLMs will be able to identify and extract the specific snippets that relate to building backlinks within these articles, which is a process made even easier when structured data is present. 

AI models will also pick up on semantic equivalencies, like the fact that the words ‘core’ and ‘midsection’ are different ways of referring to ‘abs’. 

The takeaway: exact-match keywords don’t matter nearly as much as relevance, trust, and genuinely helpful content. 

Bearing that in mind, here are some GEO techniques you should adopt as soon as possible.

Earning brand mentions on trusted websites 

If you want LLMs to trust your brand enough to recommend it, then you need to start earning brand mentions on respected websites in your field. 

According to Ahrefs, branded web mentions are the #1 visibility factor for AI search tools. 

So, instead of weighing authority through backlinks, LLMs use the editorial quality, context, and relevancy of your branded mentions online. 

Sentiment also matters, as LLMs will pay attention to the surrounding text. If a highly trusted and authoritative site mentions you but badmouths you, it will actually work against you.  

For this reason, you must ensure all your brand mentions are relevant and paint your brand in a positive light. 

Digital PR campaigns are ideal for earning the type of brand mentions that LLMs value. 

Including structured data in all content 

While structured data like schema markup helps you qualify for Rich Snippets in Google’s organic search, its importance in GEO cannot be understated. 

LLMs use semantic HTML and schema markup to quickly find things like reviews, author bios, and Q&As. 

Thus, adding structured data to your content should become a new habit, just like adding exact-match keywords was in the past. 

You can find a complete list of schemas at Schema.org. 

Creating rich topical clusters 

LLMs don’t care about Domain Authority scores, but they do care about topical authority quite a bit. If a domain is considered an authority figure on a particular topic, it can get cited above larger, more prominent brands. 

This is a significant advantage for smaller brands wanting to compete with the big dogs. 

If you want to stand out from the pack, do it with the quality and uniqueness of your content. 

Develop original research, share first-hand experiences, and find topics that other websites haven’t covered yet. 

These tactics will help you develop an edge over the competition. 

Also, aim to create content clusters. These are interlinked pieces of content that cover similar topics in great detail. 

For example, if photography was the topic, you’d create a pillar piece introducing the basics, like The Ultimate Guide to Photography and Editing. In each chapter of the piece, you’d link out to a cluster piece that fleshes out a subtopic. 

For instance, if the first subheading in your ultimate photography guide is white balancing, you can create an entire cluster piece called The Right Way to White Balance Your Camera, and link to the pillar piece. 

Internal links are the glue that form content clusters, so don’t forget to include them! 

Concluding Thoughts: The Context Revolution in Search

To sum things up, context has replaced keywords, and your GEO strategy needs to reflect that. 

AI search is the way of the future, and AI tools hold significant power over consumers (consider that they trust the recommendations of AI over those of their own friends and family). 

That means you need to transition to GEO as fast as possible. 

Are you ready to take the next step?

Book a strategy call with our team to learn how to thrive with GEO.