At Mountain Laurel Digital, we’ve spent the past year and a half closely tracking how AI is reshaping search — testing, adjusting, and learning alongside our clients. And one thing is certain: we’re not in the same SEO landscape we were six months ago. (Heck, we’re not in the same landscape we were six days ago, even!)

Google’s rollout of AI Overviews and AI Mode has already fundamentally changed how people find and interact with information online. With generative AI and agent-style search experiences on the rise, behavior has shifted toward longer, highly specific queries shaped by individual context and intent. This shift isn’t limited to Google — platforms like Bing, ChatGPT, and Perplexity are training users to expect their search tools to understand nuance, deliver synthesized answers, and even complete tasks for them.

So, what does all that mean for your SEO strategy? Chasing a handful of high-volume keywords hasn’t worked well for a while, and in today’s AI-shaped SERPs, it’s officially outdated. The internet is no longer just matching terms — it’s interpreting intent. And staying visible in this new world means thinking beyond rankings altogether to ask instead:

Is our content structured, contextual, and credible enough to be cited or stitched into dynamic AI responses?
Can our content help answer a question, guide a decision, or support an action right from the results interface?
If not, how do we adapt our content to get there?

Enter Generative Engine Optimization (GEO).

In this guide, I’ll walk you through the emerging practice of GEO — what it is, why it matters, and how it’s supported by the new discipline of relevance engineering. I’ll also explain how we as an agency are using GEO and relevance engineering to help clients update their SEO strategies, produce smarter content, and stay discoverable as the search experience continues to evolve.

But first, let’s take a quick step back and dig a little deeper into how AI has changed search to date — because the shifts have been fast, constant, and bigger than many folks realize.

How Has AI Changed SEO & Search Experiences?

The world of search didn’t change overnight. What we’re seeing today is the acceleration of trends that have been building for the past three years: more personalized results, fewer traditional blue links, and the emergence of search experiences shaped by large language models (LLMs).

Here’s a quick refresh on three of the biggest shifts: Google’s AI Overviews, the introduction of AI Mode, and the growing role of agent-style platforms like ChatGPT and Gemini.

AI Overviews

When Google’s AI Overviews began appearing in late 2024, we saw their earliest impact in B2B and SaaS — industries where Google responded to behavioral signals by prioritizing brand authority. Trusted, recognizable sources were the first to be cited or paraphrased, signaling a shift toward AI-curated answers built on perceived expertise.

As Overviews expanded, so did their impact. These AI-generated summaries now pull information from multiple sources to deliver fast, conversational answers directly in the SERP — often reducing (or entirely replacing) the need to click. That has major implications for SEO strategy: visibility no longer means ranking No. 1. It means being cited, referenced, or repurposed in the AI answer itself.

(Tip: For more guidance on how to optimize content to rank in AI Overviews, check out the AIO explainer I wrote for Search Engine Land.)

AI Mode

Google’s AI Mode (now in testing and gradually expanding) goes even further than Overviews. Instead of delivering a static set of links, AI Mode offers a conversational interface where search becomes a back-and-forth experience — more like chatting with an assistant than querying a database.

This shift raises new questions for brands and marketers, like:

What happens to traditional rankings when the AI selects and summarizes information on the fly?
How do we optimize content not just for visibility, but for interactivity and usability inside these AI flows?

If you’re curious to learn more about these changes, the most trusted resource is always going to be straight from Google. For more on what AI Mode is and where it’s headed, we recommend reading:

AI Agents

Outside of AI Mode, we’re also closely watching how search behavior is changing on platforms beyond Google — especially on Bing Copilot, ChatGPT, Perplexity, Claude, and Gemini, platforms now referred to as AI agents.

That said, Google is still the primary source of traffic for most clients, so it remains central to our optimization strategy. But we’re actively adapting our frameworks to account for how AI agents retrieve and surface information — especially as they move beyond answering questions to performing actions like booking, scheduling, or making recommendations.

Taken together, these changes signal a new era of search — one where brand visibility depends less on ranking first and more on being useful, trusted, and contextually relevant in dynamic, AI-powered environments.

That’s exactly where Generative Engine Optimization comes in. Let’s break down what it is, how it works, and how it’s helping us future-proof our clients’ strategies.

What is Generative Engine Optimization (GEO)?

GEO, Defined

Generative Engine Optimization (GEO) is the practice of optimizing content not just for traditional rankings, but for inclusion in AI-generated responses. That means tailoring your content to be understood, cited, and surfaced by LLMs — whether through Google’s AI Overviews, Bing’s Copilot, or conversational interfaces like ChatGPT and Perplexity. GEO focuses on how content is interpreted and assembled by AI systems, not just how it performs in a list of blue links.

Unlike traditional SEO, which centers on keyword targeting, backlinks, and on-page structure to win static rankings, GEO considers a wider range of signals — like topical depth, contextual clarity, schema markup, and source authority — to increase the likelihood that your content will be selected, synthesized, or referenced by AI tools.

It's not about chasing algorithm updates. It's about making your content legible and valuable to systems that are designed to predict what information a user really wants, based on how they search.

Why GEO Exists: Millions of Personalized SERPs, Not Just One

In the early days of search, everyone saw the same results for the same query. SEO strategies were built around that static reality — one search term, one SERP, one shot at visibility. But today, that model no longer reflects how search actually works.

Generative AI and personalization have fragmented the SERP. Search engines now factor in location, search history, device, language, behavior, and even content from a user’s own inbox or calendar to shape the results they see. LLMs don’t just retrieve information — they generate answers based on what they’ve learned about the user and what they predict the user wants. That means there’s no longer one search result to optimize for — there are millions, all slightly different.

GEO exists to help brands meet that complexity, optimizing for a landscape of fluid, intent-driven outputs over one idealized SERP. The goal is to increase your content’s chances of being cited, recommended, or synthesized — regardless of how the query is phrased, who’s asking it, or where it’s being asked. We’ll get into how GEO accomplishes that next.

How Does GEO Earns Visibility in AI-Driven Search Landscapes?

To make sure I’ve said this: traditional ranking factors do still play a role in overall search performance today. But they no longer tell the full story. Rather than operating on a single set of rules or outputs, AI-powered search reshapes results dynamically, based on user context, content format, and even how the prompt is phrased. That’s what makes visibility harder to predict — and more important to engineer.

GEO is the framework that helps your content show up across a wide range of possible outcomes — not just as a ranked result, but as part of an answer, a recommendation, or even an automated action. It’s about building content that’s legible, trusted, and extractable across an entire ecosystem of AI decision-making.

Let’s look at how that actually works in practice — and why the concept of relevance engineering plays a key role.

What Is Relevance Engineering? Defining GEO’s Strategic Foundation

Relevance engineering is the strategic foundation of GEO. Coined by SEO expert Mike King of iPullRank, the term refers to the art and science of optimizing content not just for traditional retrieval, but for all the ways a system might surface it: synthesis, citation, summarization, and more.

It blends technical SEO, user experience, information architecture, and content strategy — all with the goal of ensuring that your content remains visible and valuable in a search environment where no two users see the same results.

Signaling Relevance to AI Through Content Strategy

To help AI understand why your content belongs in a synthesized response, GEO and relevance engineering focus on the signals LLMs use to assess clarity, credibility, and contextual fit. At Mountain Laurel Digital, here’s how we’re approach content strategy with that in mind:

We answer intent explicitly. Content today must respond directly to the kinds of prompts AI tools receive — often phrased as questions or instructions. We design sections for client content like FAQs, summaries, or how-tos that reflect real user language and expected outcomes.
We lead with purpose. Content that meanders or buries the point is less likely to be used by AI. We prioritize early clarity: What is this page about? What question is it answering? What action is it enabling?
We establish topical depth and context. LLMs prioritize content that demonstrates both clarity and comprehensive coverage. We build content clusters, link related subpages, and support core topics with subtopics, examples, and related questions to help AI recognize the page as a well-rounded, authoritative source.
We reinforce credibility with trust signals. Author bios, citations, clear sourcing, and up-to-date timestamps all help AI systems assess whether your content is trustworthy enough to cite.
We structure content for AI interpretation. GEO means creating content that’s easy for machines to parse, extract, and reuse. Some of that ties back to the strategies above — like incorporating FAQs and internal links — but it also involves using schema markup to explicitly define your content type and writing with a clear hierarchy of headings and bullet points, so that key ideas are easy to isolate.

The more structured and context-rich your content is, the more likely it is to be selected by an AI engine assembling a dynamic response — even if you’re not ranking No. 1.

How to Build a GEO-Ready SEO Strategy

GEO requires more than repackaging old SEO tactics. To be effective in AI-shaped search environments, your strategy must be proactive, layered, and built around how today’s systems evaluate and surface content. Here are the pillars we focus on at Mountain Laurel Digital:

1. Conduct Competitive Audits for AI SERPs

Traditional SEO competitor research isn’t enough. In a GEO context, you need to understand who’s surfacing in AI-generated results and why.

What to do:

Search your target queries in AI Overviews (Google), Bing Copilot, Perplexity, and ChatGPT (with browsing enabled) to see what content is being cited or synthesized.
Note which sources, brands, or URLs are mentioned repeatedly. Analyze what makes those pages likely candidates for inclusion: clear structure? Recent publication date? Author credibility?
Create a spreadsheet to track competitors across generative platforms, including links cited, content types used (guides, FAQs, videos), and phrasing patterns in prompts and results.

Pro tip: You might not always see direct citations. Look for repeated language or specific phrasing from websites, which suggests the content was scraped and paraphrased.

2. Create Modular, Prompt-Ready Content

AI tools pull from content that maps cleanly to how people phrase questions or tasks. That means your content needs to be easy to extract, repurpose, or synthesize.

Tactical tips:

Break long content into clearly labeled sections that answer specific questions (e.g., "How do I start a nonprofit in North Carolina?")
Include summaries, step-by-step instructions, pros/cons lists, and FAQs that align with natural language prompts.
Use schema markup to explicitly define page types (e.g., how-to, FAQ, product), which increases your chances of being used as a structured answer source.

Example: Instead of a single wall of text about skincare routines, create sub-sections titled "Best Skincare for Oily Skin in Summer" or "Step-by-Step Morning Skincare Routine." That increases matchability with longer, conversational queries.

3. Prioritize Utility Over Clicks

In traditional SEO, the goal is to attract the click. In GEO, you need to provide value even if the user never visits your site. That means your content should be directly usable in the SERP.

How to approach it:

Add value-driven sections that can be cited as answers (e.g., definitions, checklists, decision trees).
Include media elements like charts, graphics, and videos that help explain concepts and are likely to be pulled into results.
Use plain language that aligns with user goals. Think: "How can I," "What should I do if," "Where can I find."

Example: A local vet clinic might create a visual guide to "What to do if your dog eats chocolate," with severity levels by weight, immediate actions, and when to call a vet. That’s content AI tools can lift and use in a synthesized emergency answer.

4. Rethink Your SEO Metrics

Traffic and rankings still matter — but they aren’t the only way to measure visibility in an AI-first landscape. GEO demands that we look at influence, usefulness, and presence differently.

Alternative metrics to track:

Citation or mention frequency in AI responses (tracked via manual testing or tools like AlsoAsked, Perplexity, etc.)
Share of voice across generative platforms
Time to value: how fast your content answers a query or supports a task
Clickless conversions: actions completed from the SERP (e.g., featured snippet usage, Google form fills)

What to shift: Start treating AI interfaces as both discovery and decision-making tools. If your content plays a role in the outcome (even without a click), that’s a win!

Final Thoughts: GEO Isn’t the Death of SEO — It’s the Evolution

Generative Engine Optimization isn’t about replacing everything you know about SEO. It’s about expanding it. The core principles still matter: quality content, technical health, user experience. But in an environment shaped by AI interpretation, not just indexing, those fundamentals need to be reimagined to stay effective.

If this all feels like a lot to take in — it is. You’re not the only one trying to make sense of all these changes, and the truth is, no one has AI-driven search completely figured out yet. That said, our team at Mountain Laurel Digital has been deep in this work for a while now, side by side with clients who are asking the same questions you are.

We may not have every answer, but we’ve learned a lot — and we’re here to think it through with you, share what’s working, and help you find the right next steps for your team.

Want to nerd out about GEO with us or take a peek at our playbook? Reach out anytime!

The Future of Generative Engine Optimization (GEO) & Relevance Engineering