Since the dawn of time, or the early 90s to be more precise, humanity has shared a strangely specific dream: the zero-click internet. No links. No tabs. No unwinnable battle against spam and pop-ups. Just ask a question and the answer appears, fully formed, like it teleported straight into your brain.

For decades, that dream lived comfortably next to flying cars and a CAPTCHA you can actually solve on the first try. Nice in theory, borderline mythical in real life. Then generative AI showed up and flipped the table. Suddenly, the way we browse the web feels optional. Information is no longer something you hunt for, hopping from page to page. It’s something you receive. Ask, read, and move on. No UI, no navigation, no emotional attachment to a browser tab you opened three hours ago.

Before you close this blog because it’s “yet another post about AI”, bear with me. This is not a tin foil hat rant about how AI will steal our jobs, melt our brains, or replace your codebase with vibes and prompts. Nor is it a love letter declaring it the greatest invention since the ad-blocker, the skip intro button, or whatever intellectuals are currently debating on Reddit.

But pretending nothing will change would be the real fantasy. Generative AI is already reshaping how people consume information, which means it will inevitably reshape how we design, build, and optimize the web itself. And yes, SEO will need to adapt, whether we like it or not.

From Searching to Asking

SEO, or Search Engine Optimization, has long been a cornerstone of web development. At its core, it’s about making content discoverable, understandable, and useful, both for search engines and for the humans behind the keyboard. Good SEO usually leads to better structure, clearer content, and more usable websites.

For years, the mental model was simple: if someone needed information, they’d Google it. That model is cracking.

Today, more and more queries skip the search results page entirely. Instead of typing keywords and scrolling through links, users prompt an LLM and get a synthesized answer instantly. Even when they do use a traditional search engine, the first thing they often see is an AI-generated summary that answers the question before any link gets a chance. We’re already at the stage where “ChatGPT” is starting to behave like a verb, following the same linguistic path “Google” took years ago.

The result is subtle but clear. Being ranked is no longer the same as being read. In this new landscape, many websites are no longer speaking directly to a human as their primary reader. They’re increasingly being read, interpreted, summarized, and quoted by intermediaries: LLMs, AI-powered search assistants, crawlers, and aggregators.

That shift is where GEO, Generative Engine Optimization, starts to matter. GEO can be thought of as a sibling to SEO. While SEO focuses on making content easy to discover and rank, GEO focuses on making content easy to understand, extract, and reuse by generative systems. The goal isn’t to replace human-friendly content with robot-speak, but to ensure that when an LLM looks at your site, it can confidently process it, reason about it, and quote it correctly.

The discipline is still young, and many of its best practices are still being defined. But even today, there are concrete steps we can take to make our content more resilient and more visible in a world where answers increasingly come from machines before they ever reach a screen.

Make things understandable

The first rule of making your site usable by generative engines is surprisingly simple: make things easy to understand. That doesn’t mean dumbing content down or avoiding technical detail. It means avoiding unnecessary complexity. Don’t hide important information behind clever wording, overloaded abstractions, or convoluted structure. Focus on communicating the core idea clearly and directly.

This is where some traditional SEO habits start to show their limits.

Don’t Repeat Yourself

Take keyword stuffing, for example. In classic SEO, repeating variations of the same concept and its synonyms increased the odds that a page would match a user’s exact search query. It makes sense, if someone searched for any of those terms, your page had a chance to surface.

LLMs don’t work that way. Generative models reason over meaning, not exact keyword matches. When a page repeats the same idea using multiple interchangeable terms, it doesn’t increase relevance. It increases noise. More tokens to process, more semantic overlap to resolve, and more work to extract a clean answer. In practice, this makes your content less attractive to summarize than a simpler, more direct alternative, even if that alternative is objectively less complete.

That doesn’t mean you should avoid technical language. Quite the opposite. When you’re writing about a specific domain, using precise, domain-specific terminology often helps. Clear technical terms reduce ambiguity, align better with expert level prompts, and tend to produce more accurate generations. The goal isn’t to avoid jargon, but to use the right jargon, intentionally and consistently.

Be authoritative

When introducing a section, avoid vague or overly short titles. Instead of labeling a section with something like “Best practices”, rewrite it as a full, explicit question. For example, rather than “Best practices for GEO”, try using “What are the best practices for GEO optimization?”.

This kind of structure does two things at once. It clearly signals the intent of the section, and it frames the content as a direct answer to a question, which is exactly the format generative systems are optimized to extract and reuse.

The underlying goal is authority. Not in a marketing sense, but in a structural one. Content that is clear, direct, well scoped, and confidently phrased is easier for LLMs to trust, summarize, and quote. And the easier your content is to reason about, the more likely it is to survive the jump from page to prompt.

Presentation matters

What you say matters. How you present it matters just as much.

Generative engines don’t experience your site the way humans do. They don’t skim, get bored, or admire your typography. They parse, extract, compare, and recombine. The easier your content is to scan and structure, the easier it is for an LLM to reuse it. A few presentation choices can make a real difference.

Be explicit with numbers and facts

Concrete data travels well through generative systems. Percentages, prices, dates, quantities, limits, and measurable outcomes are easy to extract and hard to misinterpret. If your content includes numbers, don’t hide them inside vague text.

Clear statements like pricing, performance improvements, shipping times, adoption metrics, or usage limits give LLMs something precise to work with. These details often end up enriching generated answers, summaries, and comparisons. This isn’t about exaggerating metrics or adding vanity numbers. It’s about making factual information obvious and structured.

Reduce walls of text

Long paragraphs aren’t wrong, but they’re rarely optimal. LLMs are good at synthesizing information from multiple formats, especially when ideas are broken into smaller, well-defined units. Lists, short sections, tables, and clearly scoped paragraphs are easier to process than dense blocks of text trying to do everything at once.

One particularly effective pattern, as mentioned earlier, is framing content as questions. FAQ sections work well because they mirror how users prompt generative systems. Each question clearly defines intent, and each answer cleanly scopes the information that follows. That structure makes it easier for an LLM to lift the right piece of content when generating a response.

Use media intentionally

Images, diagrams, charts, videos, and audio can all add value, as long as they’re used appropriately. Audiovisual assets help clarify complex ideas and provide additional context that can be referenced or summarized. They also improve accessibility for users with different needs, devices, or environments, which is a win regardless of GEO considerations.

Just make sure media is supported by descriptive text, captions, or surrounding explanations. Generative systems rely heavily on context, and unlabeled visuals are much less useful than annotated ones.

Back up what you say

Finally, credibility matters. Citing sources, linking to original research, standards, or authoritative documentation increases trust. For humans, citations signal rigor. For generative systems, they provide grounding. Content that is well sourced is easier to verify, contextualize, and more likely to be reused when an LLM needs to generate an answer confidently.

Think about your user

Optimizing for generative engines does not mean abandoning SEO, and it definitely doesn’t mean forgetting about your users. In fact, many of the practices that make a site good for humans still translate well to GEO, and they likely always will.

Focus on relevant information

When designing a page, start with the user. Think about their goals, their questions, and the problems they’re trying to solve. What information do they actually need? What are they likely to look for first? Then present that information clearly and predictably.

Avoid noise. Avoid unnecessary tangents. Avoid overwhelming users with more information than they can reasonably process at once. Clarity isn’t about saying less, it’s about saying the right things at the right moment.

Structure is key

Take every important piece of information and give it a clear place. Group related data. Use headings, lists, tables, and sections to make content easy to scan and easy to interpret. When information is easy for a human to find and understand, it’s usually easier for a generative system to extract and reason about as well.

In other words, accessibility for users often translates to accessibility for LLMs.

Performance matters too

Fast pages improve user experience, reduce friction, and increase engagement. They also make it easier for crawlers and automated systems to fetch and process content efficiently. While LLMs don’t “prefer” pages in the same way search engines rank them, slow, bloated, or unstable pages are still more likely to be skipped, truncated, or underutilized by the systems that ingest web content.

Conclusion

Times are changing. AI isn’t a future concept anymore, it’s already part of our everyday lives. That changes some assumptions about how content is discovered and how people interact with our products. As software developers, this change is worth paying attention to in order to grow and adapt.

The good news is that this doesn’t require throwing everything away. We don’t need to reinvent our entire design or development process. The fundamentals still hold: design for humans first, identify the information that matters, and structure it clearly. Once that foundation is in place, we can refine and enhance our content so it’s not only readable by people, but also understandable and reusable by generative systems. Done right, this gives us the best of both worlds and allows our content to reach users wherever answers are being generated.

GEO is just the beginning. As information discovery continues to fragment, new optimization layers are emerging to cover a broader range of surfaces. One example is Omnisearch Optimization (OSO), a more general approach that expands beyond traditional search and generative engines to include platforms like social media, marketplaces, and other algorithm-driven discovery systems.

This is only an introduction to GEO. The concept is still young, the practices are still evolving, and many of the rules are being written in real time. As generative systems mature, new patterns, constraints, and opportunities will inevitably emerge.

What’s certain is that the web continues to evolve. And for those willing to adapt, experiment, and rethink how content is structured and shared, the future isn’t something to fear. It’s something exciting that we should look forward to.