How to Do Generative Engine Optimization of a Website (GEO)

Search is no longer limited to ranking pages and blue links. Today, users increasingly receive direct, AI-generated answers in Google AI Overviews, Bing Copilot, ChatGPT, Perplexity, etc. These answers are not created from scratch – they are assembled from existing websites that AI systems consider reliable, clear, and reusable.

This shift changes the role of websites. Instead of competing only for clicks, websites now compete to become sources for AI-generated answers. Generative Engine Optimization (GEO) focuses on exactly this: making website content understandable, extractable, and trustworthy for AI systems.

My name is Kirill Yandovskiy, and I’m an SEO expert. I’ve spent over 15 years working with international sites in the US, Europe, the Middle East, and Asia. I have worked on long-term SEO projects in tough fields like legal services, healthcare, and B2B. In this article, I cover the practical steps required to prepare a website for AI-generated answers, from baseline visibility analysis to content audits, technical checks, competitor prompt research, and ongoing monitoring.

How AI Systems Select Sources for Generated Answers

To optimize a website for AI-generated answers, it is important to understand one practical point: AI systems do not “rank” pages the same way search engines do. Instead, they select fragments of information that can be safely reused to answer a specific question.

In practice, this selection is based on a limited set of signals that website owners can influence. These signals are not abstract or theoretical – they are directly tied to how content is written, structured, and supported.

Signals AI systems can reuse in answers

AI-generated answers rely on content that is explicit and self-contained. Pages that perform well typically include:

• clear definitions,
• direct answers stated early in the text,
• short explanatory blocks that do not depend on surrounding context.

For example, when a page answers a question in one or two precise paragraphs, AI systems can reuse that information without reconstructing meaning from multiple sections. Vague introductions, indirect explanations, or heavily narrative content are far less likely to be extracted.

From a GEO perspective, the key question is not “Is the content comprehensive?” but rather “Can a specific part of this page stand alone as an answer?”

The role of structure and formatting

Structure plays a critical role in how AI systems interpret content. Clear headings, lists, and distinct sections help models see the division between ideas. 

Pages that perform well often follow a familiar structure:

• one section – one idea,
• headings that reflect actual questions or answer topics,
• lists and bullet points used to break down processes or criteria.

Poor structure creates friction.Long paragraphs, mixed topics in one section, or too much formatting make it harder for AI to find content that can be reused. If this happens, even correct data might be missed because it’s hard to get out.

Trust, sources, and factual consistency

AI systems strongly favor content that appears reliable and consistent with other authoritative sources. This does not mean copying competitors, but it does mean avoiding contradictions and unsupported claims.

In practice, trust signals include:

• references to data, statistics, or official regulations,
• consistent terminology across the page,
• transparent authorship and subject-matter focus.

Content that sticks to well-known facts and gives clear, specific info is more likely to be reused. But pages that are mostly opinions, marketing stuff, or things that aren’t checked don’t get picked often, even if they show up high in search results.

From a GEO standpoint, trust is not built through branding language. It is built through precision, restraint, and factual grounding.

The GEO Optimization Process

Getting your website ready for GEO isn’t just a quick fix. It’s more like a plan where you figure out how AI looks at your site now, then work on the content and tech stuff, and keep an eye on things as you go.

Typical search engine improvement tracks how you rank and how much traffic you get. GEO also wants metrics that show how AI uses and mentions your website. For this reason, the process always begins with a baseline analysis. Without it, any changes made to content or structure cannot be reliably evaluated.

Step 1: AI Visibility Baseline Analysis

Before changing anything on a website, it is critical to understand whether AI systems already reference it – and if so, how and where. Many websites assume they have no AI visibility simply because they do not see obvious traffic spikes. In reality, AI citations often exist long before measurable traffic appears.

Baseline analysis establishes a starting point and prevents decisions based on guesswork.

How to measure current AI visibility

The first task is to identify whether the website appears in AI-generated answers at all. This includes:

• Google AI Overviews,
• ChatGPT (with browsing or citations),
• Bing Copilot,
• Perplexity and similar systems.

At this stage, the goal is not scale but presence. Even a small number of citations indicates that AI systems already consider the website usable in certain contexts. These early signals are especially valuable, as they often point to content patterns that can be replicated across the website.

In practice, visibility is checked by:

• testing representative prompts related to the website’s core topics,
• tracking whether the website appears among cited sources,
• noting which pages and content blocks are referenced.

This creates an initial map of how AI systems currently interpret the website.

What counts as a meaningful AI citation

Not all mentions are equally useful. A meaningful AI citation has three characteristics:

• it references a specific page rather than the domain in general,
• it appears consistently for similar prompts,
• it is placed in the main body of the generated answer, not as an afterthought.

Single, non-repeatable mentions are usually noise. Consistent citations for related questions indicate that AI systems have identified a stable information pattern on the website.

During baseline analysis, it is important to record:

• which prompts trigger citations,
• which pages are reused,
• what type of information is extracted (definitions, explanations, steps).

This data becomes the reference point for all further GEO work. Without it, improvements cannot be measured objectively, and successful changes may be mistaken for coincidence rather than cause.

Step 2: Content GEO Audit

After establishing the baseline level of AI visibility, the next step is to assess whether the website’s content can actually be reused by AI systems when generating answers. In many cases, sites contain large volumes of content, but only a small portion of it is suitable for extraction and citation.

A content GEO audit focuses on how well pages function as sources of answers, not as ranking assets.

Checking whether pages provide direct answers

The first task is to determine whether a page clearly answers a specific question. Content that performs well in AI-generated answers typically states the answer early, in clear and unambiguous terms.

During the audit, pages are reviewed to identify:

• whether the main question is answered directly,
• how quickly the answer appears within the section,
• whether the explanation depends on the surrounding context.

Sections where answers are implied rather than stated explicitly are flagged for restructuring. Usually, this just means rearranging what’s already there, not adding anything new.

Identifying missing factual and explanatory blocks

AI systems prefer content that combines clarity with verifiable information. Pages that offer only general explanations without factual support are less likely to be reused.

A content GEO audit identifies gaps such as:

• missing definitions of core terms,
• lack of concrete examples or use cases,
• absence of data points, dates, or references where they are expected.

These gaps are documented at the section level. The goal is not to overload content with statistics, but to ensure that critical statements can be validated and understood independently.

Structural issues that prevent AI reuse

Even accurate content may be ignored if it is poorly structured. During the audit, structural issues are identified that make content difficult to extract, including:

• multiple topics grouped under a single heading,
• long paragraphs covering unrelated ideas,
• headings that do not reflect the actual content of the section.

Such issues are typically resolved by splitting sections, refining headings, and aligning each section with a single, clearly defined idea.

Output: GEO content briefs for copywriters

The result of a content GEO audit is a set of actionable content briefs for copywriters. Each brief translates audit findings into specific tasks, including:

• where direct answer blocks should be added,
• which sections require factual clarification or definitions,
• how headings and structure should be adjusted for AI reuse.

Unlike traditional SEO briefs, GEO content briefs are not centered around keyword placement. Their primary purpose is to guide copywriters in reshaping content so that AI systems can reliably extract and reuse information when generating answers.

Step 3: Technical GEO Audit

Even great content might not show up in AI answers if tech issues stop AI from reading it. A technical GEO audit fixes these problems, making sure your content is easy to read and use.

GEO audits are different from regular tech SEO checks. Instead of just looking at crawl budgets or how deep indexing goes, GEO audits make sure AI can actually get to and use your content without issue.

Ensuring content is accessible to AI crawlers

The first priority is confirming that AI crawlers can access the website without restrictions. In practice, this involves reviewing:

• robots.txt rules that may block AI-related user agents,
• the presence and configuration of llms.txt or similar directives,
• server responses and HTTP status codes for key pages.

In several real cases, AI visibility issues were caused not by content quality, but by unintentional crawler blocks. Pages that are technically accessible to search engines may still be partially or fully invisible to AI systems if these configurations are incorrect.

Static content, rendering, and performance checks

AI systems strongly prefer content that is available in the initial HTML response. Pages that depend on client-side rendering, delayed loading, or interactive triggers often expose only fragments of their content to AI crawlers.

During a technical GEO audit, the following aspects are reviewed:

• whether core textual content is present without executing JavaScript,
• consistency of HTML structure across page loads,
• page performance on both desktop and mobile devices.

Page speed and stability matter, not just for users, but also for AI. If your pages load slowly or are unstable, AI systems might skip them in favor of faster, more reliable sources with similar info.

Output: Technical GEO tasks for developers

The outcome of a technical GEO audit is a set of concrete implementation tasks for developers. These tasks translate audit findings into clear actions, such as:

• making critical content available in static HTML,
• removing or adjusting crawler access restrictions,
• improving rendering stability and page performance.

Without converting technical findings into developer-ready tasks, GEO improvements tend to stall. Content updates alone cannot compensate for technical barriers that prevent AI systems from reliably accessing and reusing information.

Step 4: Competitor Prompt Analysis

At this stage, the task is to understand how questions around the topic are typically formulated and which types of formulations generative systems are likely to answer. The goal is not to guess prompts intuitively, but to work with observable patterns in existing content and user behavior.

Competitor prompt analysis in GEO combines several sources. Each of them reflects a different layer of how prompts emerge and how content is structured around them.

Analyzing competitor pages with prompt research tools

One practical way to start is by analyzing competitor pages that already cover the topic in depth. Prompt research tools such as Otterly.ai are used here to extract potential prompt formulations based on the structure, topics, and explanations present on a given page.

In practice, this involves:

• selecting a specific competitor URL rather than a homepage,
• analyzing how the topic is explained on that page,
• collecting prompt-like formulations that logically follow from the content.

These formulations are not treated as confirmed user queries. Instead, they help reveal how content is framed and which questions a page implicitly answers. This makes it easier to understand what types of prompts similar content could be built around.

Collecting prompts directly from Google

Google is one of the most reliable sources for understanding how users phrase questions. Instead of relying on keyword tools, the focus is placed on how Google surfaces questions through its interface.

Common sources include:

• autocomplete suggestions when typing partial questions,
• the “People Also Ask” block, which shows complete user questions,
• related searches that reveal recurring clarification patterns.

These prompts reflect real user behavior and provide a grounded starting point for GEO work.

Using Google Search Console to identify prompt-like queries

Google Search Console helps uncover how users already search for information related to the website. While not all of these queries trigger AI-generated answers, many of them resemble GEO prompts in structure and intent.

In practice, this step includes:

• exporting query data from Search Console,
• filtering for longer, more descriptive queries,
• isolating question-based or explanatory searches.

These queries often highlight areas where the website already appears in search results but lacks content designed for AI reuse.

Reviewing external platforms for natural-language prompts

In some niches, users express their questions more clearly outside of search engines. Platforms such as Reddit, forums, and Q&A communities provide access to natural-language questions with context and detail.

When reviewing such platforms, the focus is on:

• post titles rather than comments,
• repeated question patterns across discussions,
• prompts that reflect a need to understand rather than simply to find a page.

These sources help broaden prompt coverage and prevent GEO work from being limited to classic SEO-style queries.

Step 5: Creating GEO Content Briefs Based on Prompts

Once prompt research is complete, the next step is turning those prompts into clear, actionable content briefs. This is where GEO moves from analysis to execution. Without this step, prompt research remains theoretical and does not translate into real changes on the website.

The purpose of GEO content briefs is not to describe a topic in general, but to define how a specific question should be answered so that AI systems can easily reuse that answer.

Translating prompts into content structure

Each prompt is treated as a starting point, not as a headline or a phrase to repeat in the text. The key task is to understand what the prompt is asking and how the answer should be structured.

In practice, this involves:

• identifying the core question behind the prompt,
• determining what must be answered first and what can follow as explanation,
• mapping prompts to sections or pages rather than forcing one prompt per article.

Some prompts work best as standalone articles, while others should be grouped and addressed within a single, well-structured page. The decision depends on how closely the questions are related and how much explanation is required to answer them properly.

Defining answer blocks and supporting sections

A GEO content brief always specifies where and how answers should appear on the page. This usually includes:

• a short, direct answer block placed early in the section,
• a follow-up explanation that adds context or detail,
• optional supporting elements such as examples, steps, or clarifications.

These answer blocks are designed to stand on their own. AI systems should be able to extract them without relying on surrounding sections or introductory text.

The brief also defines boundaries: what should be included in the answer and what should be excluded to avoid dilution or ambiguity.

Step 6: Monitoring AI Presence and Results

After the initial GEO work is completed, it is important to regularly recheck the website to understand how AI visibility evolves over time. Monitoring helps track whether implemented changes lead to more stable reuse in AI-generated answers and where further adjustments are required. One-time checks are not sufficient – GEO results should be observed in dynamics.

Manual prompt testing remains essential for this work, as it allows evaluating not only presence, but also accuracy and consistency. To support this process, monitoring tools are used to track AI-related visibility signals and changes over time. These tools do not replace manual checks, but help detect patterns, confirm stability, and identify cases that require closer inspection.

• Otterly – for baseline and repeat GEO checks to observe changes after updates
• Ahrefs (Brand Radar and SERP Features) – to see how often a brand appears in AI-generated answers across topics and regions, and to identify queries that trigger generative blocks
• SE Ranking (AI Overviews Tracker) – to track which keywords trigger AI Overviews and whether the website appears among cited sources
• LLMrefs – to compare how a website and its keywords are represented across different generative systems, such as ChatGPT, Perplexity, and Bing Copilot

Using these tools together makes it possible to understand where the website has already gained stable visibility in AI-generated answers and where additional opportunities for GEO improvement still exist. Monitoring in this format turns GEO into a controlled, iterative process rather than a one-time optimization effort.

Practical Cases

Case 1: Legal Service Website

The website was launched from scratch and had no organic traffic. As part of the GEO optimization, we:

• added short blocks with clear answers to key questions;
• strengthened articles with statistics and references to authoritative sources;
• implemented structured data for services and offerings;
• improved page loading speed.

The results appeared quickly. Within a few weeks, the website began to surface in ChatGPT, and the first leads followed. Generative systems reacted faster than Google: the website started appearing in AI-generated answers before any noticeable organic traffic from traditional search.

Case 2: Medical Website

In this case, the project already had baseline SEO traffic, but the цуиsite needed to be adapted for generative systems. To achieve this, we:

• carried out technical optimization to ensure pages were correctly processed by AI;
• prepared detailed content briefs with a focus on short answers and factual clarity;
• reinforced content with statistics and additional explanations;
• refined page structure to improve parsing by generative models.

As a result, Perplexity showed the strongest response, with traffic growing from zero to 22 users per day. ChatGPT also began referencing the content, though less actively at this stage. This confirms that even in the early phases of GEO optimization, medical projects can achieve tangible visibility in generative systems.

These cases demonstrate that GEO works both as an accelerator for new websites and as a strengthening tool for existing projects. The key is to avoid isolated improvements and instead approach structure, factual depth, and technical optimization as a unified system.

Conclusion

Generative Engine Optimization is no longer an experimental concept. As AI-generated answers become a primary entry point to information, websites must adapt not only to search engines, but also to how generative systems select and reuse content.

The cases above show that GEO works both as an accelerator for new websites and as a reinforcement layer for existing SEO projects. The most consistent results come from a comprehensive approach – combining clear content structure, factual accuracy, technical accessibility, and continuous monitoring. Isolated changes rarely produce stable visibility in AI-generated answers.

GEO is a process, not a one-time task. Websites that treat it as an ongoing workflow gain a measurable advantage as generative search continues to evolve.

If you want to assess your website’s current AI visibility and understand where GEO can drive growth, you can submit a request directly on the website or contact me on Telegram: @yandowski.