Developing a GEO Strategy, Using Frameworks & Implementation Roadmap

As answer engines continue to reshape how people discover information, products, and services, organizations need a structured approach to ensure visibility in this new landscape. Generative Engine Optimization (GEO) isn't just a set of tactical adjustments to existing content, it requires a comprehensive strategy that aligns with business objectives, audience needs, and the unique characteristics of AI-driven platforms.

Let's explore how to develop a robust GEO strategy framework that serves as the foundation for all your answer engine optimization efforts. This framework will help you prioritize resources, measure success, and adapt to the rapidly evolving answer engine ecosystem.

You should know one thing from the start: GEO isn’t just a one-time project. It’s an ongoing system that adapts as models evolve, competitors shift, and new answer engines emerge.

Visualize your strategy in a five-part cycle. Each phase builds on the last and feeds directly into the next. Once you complete the loop, you have new data and insights to sharpen your strategy and begin again.

Your audience uses answer engines to solve problems.

Each question or prompt brings them one step closer to a solution. Your job is to make sure that solution leads to you.

But you can't shape the answers if you don’t know the prompts. That’s why the first step in GEO is figuring out what your audience is actually asking LLMs.

The best prompts come straight from your customers. If they’re asking it in a support ticket, sales call, or online forum, you can bet they’re asking AI the same thing.

Look for patterns in:

The goal here is to build a prompt library. In other words, a list of real questions your audience asks naturally, in their own words.

Prompts aren’t isolated. They appear at different stages of the customer journey.

At this stage, the buyer tries to understand a concept or explore a problem space. They’re not ready to choose yet, but they are forming a mental shortlist of solutions.

This is where buyers weigh trade-offs. They want differentiators, proof points, and stories from real users.

Now they’re ready to act. They’re looking for trust signals and practical guidance that makes one choice feel safer than the rest.

By mapping prompts this way, you can see how questions build on each other. It helps you anticipate what comes before and after a query and design content that answers in sequence, not just in silos.

Now that you have your prompts, it’s time to see where you stand.

Let’s run a quick experiment to see if (and how) your brand appears in AI answers.

Pull up your favorite answer engine (ChatGPT, Claude, Perplexity, etc.) and ask some of the prompts you mapped out in Phase 1.

For example:

Here’s what to look for:

Now repeat the same exercise in a different answer engine. Then another. The more you test, the clearer the gaps become.

A “gap” might mean:

Start prioritizing from there. If you’re missing entirely, that’s the first fire to put out. If you show up inaccurately, focus on fixing the biggest risks to perception.

Want to know exactly where you stand in AI-generated answers?

Talk to us for a free assessment.

You don’t need to meet every inconsistency with the same urgency. Some are small glitches, some are existential threats.

Here’s a scale you can use to prioritize your focus when you audit how AI portrays your brand:

🟢 Level 1: Clean Read

🟢 Level 2: Minor Glitch

🟢 Level 3: Low-Grade Lag

🟡 Level 4: Narrative Drift

🟡 Level 5: Perception Friction

🟠 Level 6: Signal Breakdown

🔴 Level 7: Strategic Misalignment

🔴 Level 8: Brand Safety Risk

🚨 Level 9: Narrative Containment Failure

🚨 Level 10: Existential Threat

Here are some real examples to put it into context.

This one comes from the head of marketing at a popular online marketplace:

“We used to be a re-commerce app. Now we’ve got cars, services, jobs… but AI still frames us like it’s 2019.”

This one stung because the company has actually made huge progress. They’ve evolved well beyond their original category, but the models haven’t caught up.

The AI is clinging to an old label, and it’s boxing them into a narrative that’s no longer true. This can slow growth, misrepresent focus, and confuse high-intent users.

🟡 Level 4: Narrative Drift

An SEO lead at a fintech company told me she’s noticed some weird AI responses about her brand lately.

“We realized AI was pulling from third-party content that didn’t really understand us.”

At first, she thought the model was hallucinating. Turns out it was just scraping the wrong sources.

Now they’re asking: if AI shapes the narrative, who’s actually writing the script?

🟠 Level 6: Signal Breakdown

This is a really spooky one from a senior comms strategist at a major industrial brand:

“AI is pulling from Reddit drama and turning it into the default answer about our brand.”

A marketing campaign gone wrong sparked backlash for this brand a few years ago. The moment passed, but AI never let it go.

Now, that moment is stuck in the model as a default narrative. And that’s when misrepresentation starts to become a serious long-term liability.

🚨 Level 9: Narrative Containment Failure

No matter where you land on the scale, the solution starts in the same place:

Train the models before they train the world.

By now, you’ve mapped the prompts and tested your visibility. You know where you’re showing up and where you’re not.

The next step is turning those insights into a strategy you can actually execute. Start by looking at the sources behind the answers.

Every citation an AI model pulls is a breadcrumb. Follow those breadcrumbs and you’ll see the foundation for your GEO strategy.

Most of them fall into three categories:

Your job is to build a plan that covers all three.

Your owned channels are anything you have direct control over, including landing pages on your website, social media accounts, or to an extent, review sites like G2 and Capterra.

When you check the sources behind AI answers, pay attention to which of your pages show up. More often than not, it’s an old landing page or product page that hasn’t been touched in years. If that’s the case, it probably has outdated or misleading information.

The good news: this is one of the fastest wins you can get. Update the page so every detail is accurate, current, and aligned with how you want AI to represent you.

But don’t overhaul it completely. If AI already pulls from that page, it means the structure works. Keep the formatting clear and familiar so the model treats it as a reliable source.

AI also leans on public-facing profiles like LinkedIn, YouTube, and X. These often surface in response to “What is [brand]?” type prompts.

Treat your company descriptions, bios, and “about” sections as potential citations.

Quick wins here:

You don’t need to overhaul your social presence for GEO. But treating these profiles like extensions of your website makes it much more likely AI will pull the right message when people ask about you.

AI loves review sites like G2, TrustRadius, and Capterra because they’re structured, credible, and widely recognized as decision-making sources.

That means it should be a primary focus area for your brand.

Fill out every section in full. Update it whenever your product changes. (AI will happily cite outdated descriptions if that’s all it finds.)

If you know G2, you know volume is key to improving your stars and rankings.

We don’t care about stars or rankings right now.

AI cares about the content of reviews, not star count. Encourage your customers to write detailed reviews with specifics (use cases, features, outcomes). AI is way more likely to cite those passages.

Many AI answers pull from G2’s comparison page. Make sure your product is in the right categories and encourage customers to highlight differentiators in reviews.

AI models parse the whole page, including responses. Thoughtful, transparent replies signal credibility and content. It also prevents the “default narrative” from being only negative.

Just like with SEO rankings, track when and how your G2 profile shows up in AI answers.

Set prompts like, "What is the best [category] software?” and look for G2 citations. Then, optimize based on what you see.

It stings when competitors show up as sources in AI answers because it means they control the narrative, not you.

But there’s a silver lining. Every competitor's citation is a blueprint.

Their success shows you exactly what kind of content, structure, or signals the model rewards.

Here’s how to turn those competitor wins into your own:

Look closely at cited pages. How are they structured? Are they formatted cleanly with headings, definitions, tables, or comparisons? What questions are they answering that you aren’t?

If they have a comparison page, you need one too. If their FAQ shows up, your FAQ needs to be just as thorough and more up-to-date.

The goal isn’t to copy and paste. You have to meet the same informational need with sharper, fresher, and more credible content.

Competitor citations often signal volatility. Today, they show up; tomorrow, it could be you. Track which competitor sources appear most often and use them as a running benchmark.

Remember, when a competitor channel pops up, it’s a roadmap. They’re showing you the exact levers you need to pull so AI cites you instead of them.

AI likes third-party sources because they’re neutral. For example, communities like Reddit and publications like industry blogs or listicles carry outsized weight because they're seen as independent validators.

As such, it may seem like these channels are out of your control. However, with a proper strategy, you can give them a hard nudge in the right direction.

AI treats unfiltered, peer-to-peer conversations as especially credible. That’s why Reddit is one of the most frequently cited third-party sources in AI answers.

The channel is tricky, though. Communities will spot a brand cosplaying as a “fellow Redditor” immediately.

Some companies, like Ramp, for example, have gone as far as to hire a dedicated “professional Redditor” to engage authentically on the platform.

If you have the budget, this kind of role makes sense. If not, their approach still provides a useful blueprint you can adapt.

What works on Reddit:

Don’t just jump into the conversation right away. Be tactical.

Here are a few best practices to consider when engaging:

Look inside your company first. Chances are, someone is already active in the right subreddits. They don’t need to be a top 1% contributor, but they do need a legitimate account history.

If they’re willing to help, set some clear guardrails:

Reddit isn’t easy, but it’s one of the strongest credibility signals you can earn. Approach it with patience and authenticity, and the visibility gains will follow.

Publications, from major outlets to niche industry blogs, are all over AI-generated answers.

Sometimes they're the heavy hitters you would expect to carry weight in a traditional SEO strategy. But more often than not, they’re extremely niche blogs tailored to your industry.

That makes it way more likely to get in contact with someone who can help create content that influences AI to tell the story you want it to tell.

An effective outreach strategy is essential. Whether you’re correcting misinformation, requesting an update, or pitching for inclusion in a new article, the approach is the same:

Start with the author if there’s a byline. If not, look for the section editor or contributor desk. Most publications list the right contact points on their site.

Editors and journalists decide in seconds whether to open your email. A clear, concise subject line like “Correction request: [Brand]” or “Story idea: [Category] trend in 2025” outperforms vague or overly clever headlines. Clarity signals respect for their time.

The first line of your message should prove you’ve done your homework: “In your recent article on [topic]…” or “I saw you covered [competitor] last week…” Show them why your note matters to their work right now, not just to your brand.

Journalists and editors don’t have time to dig for context. Give them everything they need in one place:

Think of it as a ready-to-use “about” section. The clearer and more complete your info, the easier for them to write about you accurately.

Think long-term.

Every interaction is a relationship-building opportunity. Be easy to work with now, and you increase your chances of net-new coverage later.

Thank them when they make the change or run your piece. Stay on their radar as a reliable source, not just someone who shows up when there’s a problem.

Writing for humans is straightforward. You speak human!

Now you have to learn how to speak LLM, too.

Let’s talk about how to structure your content for AI without sacrificing the best practices that make it digestible for human readers.

Answer engines love structure, and frankly, so do human readers. Therefore, getting your structure right is a win-win and a critical step.

Here’s how to get it right:

Use a proper heading structure (H1 → H2 → H3) to reflect the flow of ideas. This helps LLMs and crawlers understand the scope and depth of each section.

For example:

Models are far more likely to pull clean, complete answers from well-scoped sections. Break your content into digestible sections to make it as easy as possible on them.

Here’s a little pro tip: Treat each section like it’s answering its one specific question.

Here’s what your sections might look like:

Make sure each chunk passes the “could this be a good answer?” test before moving on.

AI won’t always know what your most important point is supposed to be. That means you need a way to say “Hey chatbot, this is important, so pay attention!” without actually saying it outright.

Try signal phrases like:

Here’s what you need to know: AI is smart, but it still needs a little help sometimes. Make sure you know the right signals to give it a hand.

Don’t get too cute with your formatting. Your layout elements should be easy for AI and humans to parse and reuse.

Some tried-and-true formats include:

The goal is to create content that reads like an answer key. That is, its easy to follow, easy to cite, and easy to remember.

Understanding structure makes you conversational in LLM. But you have to get a bit more technical if you want to become fluent.

You don’t have to code this yourself, but you do need to know what it is, why it matters, and who on your team can make it happen.

Here’s the plan:

This will live on your website as its own landing page. It tells AI where to look and what to prioritize.

Here’s what to include:

Once you’ve drafted your llms.txt file, work with your SEO lead or developer to upload it to the root directory of your website.

That means the file should be accessible at: yourwebsite.com/llms.txt

This is similar to how websites use a robots.txt file to guide traditional search engine crawlers. But instead of telling crawlers what not to do, llms.txt tells AI models what content to prioritize and where to find it.

Schema markup is a type of structured data you can add to your web pages to help AI (and search engines) understand what your content is actually about.

For example, you can use it to clarify:

These signals make it easier for LLMs to categorize, summarize, and cite your content accurately.

You’ll use a standardized vocabulary from Schema.org, a collaborative project backed by major search engines like Google, Bing, and Yahoo.

Think of Schema.org as the dictionary that both you and AI systems use to speak the same language. You can implement the schema manually, use a plugin (if you’re on WordPress or a similar CMS), or have your dev team embed it directly in your page templates.

Make simplified, clean versions of your best pages just for AI systems.

Identify your top 10-20 pages, then turn them into markdown files. You'll need a place to host these markdown files so LLMs can easily access and crawl them.

You have two main options:

Either way, you’ll link to these markdown files from your llms.txt file so AI crawlers can find and process them efficiently.

Make sure your links make sense.

Audit your top pages and update where needed, especially before creating new content.

Don’t overlook the basics. They’re still essential for visibility.

OK, so here’s your checklist:

You don’t have to implement everything at once. But the sooner you start, the sooner AI systems will start seeing (and remembering) your content.

Content optimization is a lot of work. Why not let us do it for you? We’re here to help.

You’ve mapped your strategy, built strong content, distributed it smartly, and earned some level of AI visibility. But none of that matters unless it’s driving the outcomes you actually care about.

This phase is about staying sharp. You need to know what’s working, what isn’t, and where to double down or pivot.

You don’t need a dashboard stuffed with vanity metrics. You just need a few powerful signals that answer three key questions:

Let’s break those down.

These tell you if answer engines notice and trust you.

These signals help you understand whether you’ve broken into the model’s “mental model” and how much weight you carry once you’re in.

Visibility is great. But it has to translate into results.

These metrics show exactly how AI visibility impacts your business.

These show what content is pulling its weight and what needs work.

These insights help you refine your content strategy and double down on what’s actually working.

The next step is using this data to get even sharper over time. So, let’s talk optimization.

Think of optimization as continuous tuning. You don’t need to scrap your strategy, you just need to tighten your feedback loop and act on what the data tells you.

Here’s how:

Check in regularly to see how visible your brand still is and where. Use tools like Search Party, Perplexity, or ChatGPT to re-scan prompts in your space.

You’ll want to know:

Don’t let gains fade quietly. If an important page is no longer showing up, it’s time to investigate and refresh.

You don’t need to test everything, just what affects what LLMs see, parse, and cite.

Try experimenting with:

Always tie experiments back to the outcome: better visibility, more citations, stronger authority.

AI feedback doesn’t just live in dashboards. It shows up in real conversations.

Tune into:

If AI is shaping how people talk about you, it should shape how you optimize your content.

Answer engines are evolving fast. Set up a system for scanning the landscape and staying responsive.

Track:

The best GEO strategies aren’t static. They adapt as the landscape shifts without burning everything down.

Compile your GEO strategy into a comprehensive document that includes:

This comprehensive strategy document serves as a roadmap for all GEO activities, ensuring alignment across teams and consistent progress toward business objectives.

A comprehensive GEO strategy framework provides the foundation for effective answer engine optimization. By aligning business objectives, audience needs, content strategy, technical implementation, distribution efforts, and measurement practices, organizations can ensure their visibility in the evolving landscape of AI-driven information discovery.

Remember that GEO strategy is not a one-time exercise but an ongoing process that requires regular assessment and adaptation. As answer engines continue to evolve, your strategy should evolve with them, always keeping your business objectives and audience needs at the center.