Beyond SEO: Crafting Content for AI-Driven Search Engines

Traditional SEO has dominated the web for almost 30 years, enhancing website visibility, targeting organic traffic, and helping your products and services reach the right audience. 

Yet while SEO remains an invaluable tool for digital marketers, in under two years, artificial intelligence, particularly Large Language Models (LLMs), such as ChatGPT, has begun to change how people search online. 

Today, it's becoming increasingly important to engineer content not just for search engines, but for generative AI systems. 

This article explores why classic SEO is in decline, highlights potential new ranking techniques, and outlines ways you can improve your visibility in LLMs. 

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The Decline of Traditional SEO?

Traditional SEO is based on a familiar structure: target the right keywords, optimise meta tags, build backlinks, and climb the rankings on Google's SERPs. However, this model assumes the end goal is always a click. 

In an LLM-powered search environment, that's no longer true. 

Indeed, 40% of search queries now end without a click, with 80% of users finding answers via AI summaries. Even digital powerhouses like Mailchimp report a decline in clicks as AI answers replace webpage hits. 

A key factor in this phenomenon is that LLMs like ChatGPT, Google's SGE, and Perplexity don't aim to generate a list of links. Instead, they're programmed to create direct answers for the user, drawing context from surrounding text and prioritising trust, clarity, and structure over typical SEO signals. 

As a result, ranking #1 on Google doesn't guarantee inclusion in an AI-generated summary. For example, your content could be top of the SERPs, yet invisible in conversational search, especially if it lacks the structured, rich, and human-sounding content that LLMs prefer to cite.

Think of it like this, SEO optimises for machines that list. LLMs optimise for machines that think.

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New Visibility Models: Hype or Helpful?

Needless to say, there are already digital marketers claiming expertise in LLM optimisation through frameworks such as: 

Answer Engine Optimisation (AEO)

AEO is about creating content that can be utilised by AI assistants, rather than just being discovered by search engines.

It focuses on providing concise, well-structured answers that language models can easily summarise, paraphrase, or cite. Optimising for AEO often involves using a natural, conversational tone, breaking content into clear Q&A formats, and applying Schema markup (especially FAQ, HowTo, and Product schemas).

This method suggests using helpful bullets, headings that double as questions, and a structure that mimics how someone might ask and answer a real-life query. 

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Artificial Intelligence Optimisation (AIO)

AIO professes to go deeper into how LLMs interpret and surface your content. 

Instead of focusing on traditional SEO signals, AIO considers how AI models combine, embed, and retrieve information. The goal isn't to rank higher, per se, but to be selected as a trustworthy, token-efficient source within an LLM's neural pathways.

That means prioritising clarity, consistency, and authority by using credible sources, original insights, and language that aligns with how people naturally express questions or problems. 

According to AIO frameworks, clean layouts, unambiguous headers, and embedded expertise (like quotes or data) all improve your chances of being cited in an AI response.

But, despite the buzz, there's little evidence that these frameworks deliver results. At least not yet.

Because, as Get Stuff Digital (GSD) points out in this informative article, the empirical evidence suggests that there is no official method to optimise for LLMs.

Instead, they suggest the following approach, known as Generative Engine Optimisation (GEO). 

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Lessons from Real Testing 

Get Stuff Digital's research included structured tests using multiple LLMs and site types to analyse what kinds of content got cited and surfaced in responses. They prioritised:

• Authority-driven websites
• Pages placed on heavily cited lists
• Integration of Reddit mentions

By aligning with the trusted domains, formats, and Reddit links favoured by LLMs, GSD were able to increase visibility in LLM responses. 

As such, recommendations for Generative Engine Optimisation include:
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Invest in formats LLMs already favour

• Create fluff-free, helpful listicles. 
• Host side-by-side product comparisons.
• Publish client reviews and case studies.

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Use Reddit

• Reddit is a powerful tool for promoting AI visibility.
• Start threads, offer genuine value, and aim for organic brand mentions.

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Build on what already works in SEO

• Create helpful, focused, trustworthy content.
• Use strong headlines, a logical structure, and a clean user experience. 

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Treat LLMs like emerging search engines

• Trust remains a key signal for LLMs. Backlinks, credible sources, case studies, and expert citations still matter.
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Be discerning.

• There are AI-hype tools and plugins a-plenty online. While many are gimmicky, there are some useful resources out there, such as this bookmarklet from The SEO Pub. Choose well-researched, well-reasoned content from reputable sources over marketing buzz. 

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What does the future hold for SEO? 

Traditional SEO still matters. After all, LLMs depend on existing web content to answer search queries. However, as AI tools like ChatGPT, SGE, and Perplexity redefine how people search, ranking alone won't guarantee visibility. 

For example, Google's AI Overviews typically appear for top-of-funnel, informational queries. While they often draw from content already ranking highly (three-quarters come from positions 1–12), the volatile nature of these overviews makes consistent inclusion difficult to guarantee. That's why your content needs to answer queries clearly and immediately, using question-led headings, FAQ schema, and clean structure.

ChatGPT, on the other hand, sources results via Bing, where exact keyword match, strong meta titles, freshness, and social media signals influence what is crawled. Pages with clear, descriptive titles, accurate metadata, and regular updates are more likely to be selected.

Ultimately, new frameworks like AEO, AIO, and GEO offer useful reference points, but they don’t replace the basics. Strong content, structured data, and strategic updates remain essential.

In other words, don't write for the algorithm. Write for the answer. Clarity wins in the long run.