If you look at job postings on Indeed and LinkedIn, you’ll see a wave of acronyms added to the alphabet soup as companies try to hire people to boost visibility on large language models (LLMs).

Some people are calling it generative engine optimization (GEO). Others call it answer engine optimization (AEO). Still others call it artificial intelligence optimization (AIO). I prefer large model answer optimization (LMAO).

I find these new acronyms a bit ridiculous because while many like to think AI optimization is new, it isn’t. It’s just long-tail SEO — done the way it was always meant to be done.

Why LLMs still rely on search

Most LLMs (e.g., GPT-4o, Claude 4.5, Gemini 1.5, Grok-2) are transformers trained to do one thing: predict the next token given all previous tokens.

AI companies train them on massive datasets from public web crawls, such as:

• Common Crawl.
• Digitized books.
• Wikipedia dumps.
• Academic papers.
• Code repositories.
• News archives.
• Forums.

The data is heavily filtered to remove spam, toxic content, and low-quality pages. Full pretraining is extremely expensive, so companies run major foundation training cycles only every few years and rely on lighter fine-tuning for more frequent updates.

So what happens when an LLM encounters a question it can’t answer with confidence, despite the massive amount of training data?

AI companies use real-time web search and retrieval-augmented generation (RAG) to keep responses fresh and accurate, bridging the limits of static training data. In other words, the LLM runs a web search.

To see this in real time, many LLMs let you click an icon or “Show details” to view the process. For example, when I use Grok to find highly rated domestically made space heaters, it converts my question into a standard search query.

Dig deeper: AI search is booming, but SEO is still not dead

The long-tail SEO playbook is back

Many of us long-time SEO practitioners have praised the value of long-tail SEO for years. But one main reason it never took off for many brands: Google.

As long as Google’s interface was a single text box, users were conditioned to search with one- and two-word queries. Most SEO revenue came from these head terms, so priorities focused on competing for the No. 1 spot for each industry’s top phrase.

Many brands treated long-tail SEO as a distraction. Some cut content production and community management because they couldn’t see the ROI. Most saw more value in protecting a handful of head terms than in creating content to capture the long tail of search.

Fast forward to 2026. People typing LLM prompts do so conversationally, adding far more detail and nuance than they would in a traditional search engine. LLMs take these prompts and turn them into search queries. They won’t stop at a few words. They’ll construct a query that reflects whatever detail their human was looking for in the prompt.

Suddenly, the fat head of the search curve is being replaced with a fat tail. While humans continue to go to search engines for head terms, LLMs are sending these long-tail search queries to search engines for answers.

While AI companies are coy about disclosing exactly who they partner with, most public information points to the following search engines as the ones their LLMs use most often:

• ChatGPT – Bing Search.
• Claude – Brave Search.
• Gemini – Google Search.
• Grok – X Search and its own internal web search tool.
• Perplexity – Uses its own hybrid index.

Right now, humans conduct billions of searches each month on traditional search engines. As more people turn to LLMs for answers, we’ll see exponential growth in LLMs sending search queries on their behalf.

SEO is being reborn.

Dig deeper: Why ‘it’s just SEO’ misses the mark in the era of AI SEO

How to do long-tail SEO with help from AI

The principles of long-tail SEO haven’t changed much. It’s best summed up by Baseball Hall of Famer Wee Willie Keeler: “Keep your eye on the ball and hit ’em where they ain’t.”

Success has always depended on understanding your audience’s deepest needs, knowing what truly differentiates your brand, and creating content at the intersection of the two.

As straightforward as this strategy has been, few have executed it well, for understandable reasons.

Reading your customers’ minds is hard. Keyword research is tedious. Content creation is hard. It’s easy to get lost in the weeds.

Happily, there’s someone to help: your favorite LLM.

Here are a few best practices I’ve used to create strong long-tail content over the years, with a twist. What once took days, weeks, or even months, you can now do in minutes with AI.

1. Ask your LLM what people search when looking for your product or service

The first rule of long-tail SEO has always been to get into your audience’s heads and understand their needs. This once required commissioning surveys and hiring research firms to figure out.

But for most brands and industries, an LLM can handle at least the basics. Here’s a sample prompt you can use.

Act as an SEO strategist and customer research analyst. You're helping with long-tail keyword discovery by modeling real customer questions.

I want to discover long-tail search questions real people might ask about my business, products, and industry. I’m not looking for mere keyword lists. Generate realistic search questions that reflect how people research, compare options, solve problems, and make decisions.

Company name: [COMPANY NAME]
Industry: [INDUSTRY]
Primary product/service: [PRIMARY PRODUCT OR SERVICE]
Target customer: [TARGET AUDIENCE]
Geography (if relevant): [LOCATION OR MARKET]

Generate a list of 75 – 100 realistic, natural-language search queries grouped into the following categories:

AWARENESS
• Beginner questions about the category
• Problem-based questions (pain points, frustrations, confusion)

CONSIDERATION
• Comparison questions (alternatives, competitors, approaches)
• “Best for” and use-case questions
• Cost and pricing questions

DECISION
• Implementation or getting-started questions
• Trust, credibility, and risk questions

POST-PURCHASE
• Troubleshooting questions
• Optimization and advanced/expert questions

EDGE CASES
• Niche scenarios
• Uncommon but realistic situations
• Advanced or expert questions

Guidelines:
• Write queries the way real people search in Google or ask AI assistants.
• Prioritize specificity over generic keywords.
• Include question formats, “how to” queries, and scenario-based searches.
• Avoid marketing language.
• Include emotional, situational, and practical context where relevant.
• Don't repeat the same query structure with minor variations.
• Each query should suggest a clear content angle.

Output as a clean bullet list grouped by category.

You can tweak this prompt for your brand and industry. The key is to force the LLM (and yourself) to think like a customer and avoid the trap of generating keyword lists that are just head-term variations dressed up as long-tail queries.

With a prompt like this, you move away from churning out “keyword ideas” and toward understanding real customer needs you can build useful content around.

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2. Use your LLM to analyze your search data

Most large brands and sites don’t realize they’ve been sitting on a treasure trove of user intelligence: on-site search data.

When customers type a query into your site’s search box, they’re looking for something they expect your brand to provide.

If you see the same searches repeatedly, it usually means one of two things:

• You have the information, but users can’t find it.
• You don’t have it at all.

In both cases, it’s a strong signal you need to improve your site’s UX, add meaningful content, or both.

There’s another advantage to mining on-site search data: it reveals the exact words your audience uses, not the terms your team assumes they use.

Historically, the challenge has been the time required to analyze it. I remember projects where I locked myself in a room for days, reviewing hundreds of thousands of queries line by line to find patterns — sorting, filtering, and clustering them by intent.

If you’ve done the same, you know the pattern. The first few dozen keywords represent unique concepts, but eventually you start seeing synonyms and variations.

All of this is buried treasure waiting to be explored. Your LLM can help. Here’s a sample prompt you can use:

You're an SEO strategist analyzing internal site search data.

My goal is to identify content opportunities from what users are searching for on my website – including both major themes and specific long-tail needs within those themes.

I have attached a list of site search queries exported from GA4. Please:

STEP 1 – Cluster by intent
Group the queries into logical intent-based themes.

STEP 2 – Identify long-tail signals inside each theme
Within each theme:
• Identify recurring modifiers (price, location, comparisons, troubleshooting, etc.)
• Identify specific entities mentioned (products, tools, features, audiences, problems)
• Call out rare but high-intent searches
• Highlight wording that suggests confusion or unmet expectations

STEP 3 – Generate content ideas
For each theme:
• Suggest 3 – 5 content ideas
• Include at least one long-tail content idea derived directly from the queries
• Include one “high-intent” content idea
• Include one “problem-solving” content idea

STEP 4 – Identify UX or navigation issues
Point out searches that suggest:
• Users cannot find existing content
• Misleading navigation labels
• Missing landing pages

Output format:
Theme:
Supporting queries:
Long-tail insights:
Content opportunities:
UX observations:

Again, customize this prompt based on what you know about your audience and how they search.

The detail matters. Many SEO practitioners stop at a prompt like “give me a list of topics for my clients,” but this pushes the LLM beyond simple clustering to understand the intent behind the searches.

I used on-site search data because it’s one of the richest, most transparent, and most actionable sources. But similar prompts can uncover hidden value in other keyword lists, such as “striking distance” terms from Google Search Console or competitive keywords from Semrush.

Even better, if your organization keeps detailed customer interaction records (e.g., sales call notes, support tickets, chat transcripts), those can be more valuable. Unlike keyword datasets, they capture problems in full sentences, in the customer’s own words, often revealing objections, confusion, and edge cases that never appear in traditional keyword research.

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3. Create great content

The next step is to create great content.

Your goal is to create content so strong and authoritative that it’s picked up by sources like Common Crawl and survives the intense filtering AI companies apply when building LLM training sets. Realistically, only pioneering brands and recognized authorities can expect to operate in this rarefied space.

For the rest of us, the opportunity is creating high-quality long-tail content that ranks at the top across search engines — not just Google, but Bing, Brave, and even X.

This is one area where I wouldn’t rely on LLMs, at least not to generate content from scratch.

Why?

LLMs are sophisticated pattern matchers. They surface and remix information from across the internet, even obscure material. But they don’t produce genuinely original thought.

At best, LLMs synthesize. At worst, they hallucinate.

Many worry AI will take their jobs. And it will — for anyone who thinks “great content” means paraphrasing existing authority sources and competing with Wikipedia-level sites for broad head terms. Most brands will never be the primary authority on those terms. That’s OK.

The real opportunity is becoming the authority on specific, detailed, often overlooked questions your audience actually has. The long tail is still wide open for brands willing to create thoughtful, experience-driven content that doesn’t already exist everywhere else.

We need to face facts. The fat head is shrinking. The land rush is now for the “fat tail.” Here’s what brands need to do to succeed:

Dominate searches for your brand

Search your brand name in a keyword tool like Semrush and review the long-tail variations people type into Google. You’ll likely find more than misspellings. You’ll see detailed queries about pricing, alternatives, complaints, comparisons, and troubleshooting.

If you don’t create content that addresses these topics directly — the good and the bad — someone else will. It might be a Reddit thread from someone who barely knows your product, a competitor attacking your site, a negative Google Business Profile review, or a complaint on Trustpilot.

When people search your brand, your site should be the best place for honest, complete answers — even and especially when they aren’t flattering. If you don’t own the conversation, others will define it for you.

The time for “frequently asked questions” is over. You need to answer every question about your brand—frequent, infrequent, and everything in between.

Go long

Head terms in your industry have likely been dominated by top brands for years. That doesn’t mean the opportunity is gone.

Beneath those competitive terms is a vast layer of unbranded, long-tail searches that have likely been ignored. Your data will reveal them.

Review on-site search, Google Search Console queries, customer support questions, and forums like Reddit. These are real people asking real questions in their own words.

The challenge isn’t finding questions to write about. It’s delivering the best answers — not one-line responses to check a box, but clear explanations, practical examples, and content grounded in real experience that reflects what sets your brand apart.

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Expertise is now a commodity: Lean into experience, authority, and trust

Publishing expert content still matters, but its role has changed. Today, anyone can generate “expert-sounding” articles with an LLM.

Whether that content ranks in Google is increasingly beside the point, as many users go straight to AI tools for answers.

As the “expertise” in E-E-A-T becomes table stakes, differentiation comes from what AI and competitors can’t easily replicate: experience, authority, and trust.

That means publishing:

• Original insights and genuine thought leadership from people inside your company.
• Real customer stories with measurable outcomes.
• Transparent reviews and testimonials.
• Evidence that your brand delivers what it promises.

This isn’t just about blog content. These signals should appear across your site — from your About page to product pages to customer support content. Every page should reinforce why a real person should trust your brand.

Stop paywalling your best content

I’m seeing more brands put their strongest content behind logins or paywalls. I understand why. Many need to protect intellectual property and preserve monetization. But as a long-term strategy, this often backfires.

If your content is truly valuable, the ideas will spread anyway. A subscriber may paraphrase it. An AI system may summarize it. A crawler may access it through technical workarounds. In the end, your insights circulate without attribution or brand lift.

When your best content is publicly accessible, it can be cited, linked to, indexed, and discussed. That visibility builds authority and trust over time.

In a search- and AI-driven ecosystem, discoverability often outweighs modest direct content monetization.

This doesn’t mean content businesses can’t charge for anything. It means being strategic about what you charge for. A strong model is to make core knowledge and thought leadership open while monetizing things such as:

• Tools.
• Community access.
• Premium analysis or data.
• Courses or certifications.
• Implementation support.
• Early access or deeper insights.

In other words, let your ideas spread freely and monetize the experience, expertise, and outcomes around them.

Stop viewing content as a necessary evil

I still see brands hiding content behind CSS “read more” links or stuffing blocks of “SEO copy” at the bottom of pages, hoping users won’t notice but search engines will.

Spoiler alert: they see it. They just don’t care.

Content isn’t something you add to check an SEO box or please a robot. Every word on your site must serve your customers. When content genuinely helps users understand, compare, and decide, it becomes an asset that builds trust and drives conversions.

If you’d be embarrassed for users to read your content, you’re thinking about it the wrong way. There’s no such thing as content that’s “bad for users but good for search engines.” There never was.

Embrace user-generated content

No article on long-tail SEO is complete without discussing user-generated content. I covered forums and Q&A sites in a previous article (see: The reign of forums: How AI made conversation king), and they remain one of the most efficient ways to generate authentic, unique content.

The concept is simple. You have an audience that’s already passionate and knowledgeable. They likely have more hands-on experience with your brand and industry than many writers you hire. They may already be talking about your brand offline, in customer communities, or on forums like Reddit.

Your goal is to bring some of those conversations onto your site.

User-generated content naturally produces the long-tail language marketing teams rarely create on their own. Customers

• Describe problems differently.
• Ask unexpected questions.
• Compare products in ways you didn’t anticipate.
• Surface edge cases, troubleshooting scenarios, and real-world use cases that rarely appear in polished marketing copy.

This is exactly the kind of content long-tail SEO thrives on.

It’s also the kind of content AI systems and search engines increasingly recognize as credible because it reflects real experience rather than brand messaging many dismiss as inauthentic.

Brands that do this well don’t just capture long-tail traffic. They build trust, reduce support costs, and dominate long-tail searches and prompts.

In the age of AI-generated content, real human experience is one of the strongest differentiators.

The new SEO playbook looks a lot like the old one

For years, SEO has been shaped by the limits of the search box. Short queries and head terms dominated strategy, and long-tail content was often treated as optional.

LLMs are changing that dynamic. AI is expanding search, not eliminating it.

AI systems encourage people to express what they actually want to know. Those detailed prompts still need answers, and those answers come from the web.

That means the SEO opportunity is shifting from competing over a small set of keywords to becoming the best source of answers to thousands of specific questions.

Brands that succeed will:

• Deeply understand their audience.
• Publish genuinely useful content.
• Build trust through real engagement and experience.

That’s always been the recipe for SEO success. But our industry has a habit of inventing complex tactics to avoid doing the simple work well.

Most of us remember doorway pages, exact match domains, PageRank sculpting, LSI obsession, waves of auto-generated pages, and more. Each promised an edge. Few replaced the value of helping users.

We’re likely to see the same cycle repeat in the AI era.

The reality is simpler. AI systems aren’t the audience. They’re intermediaries helping humans find trustworthy answers.

If you focus on helping people understand, decide, and solve problems, you’re already optimizing for AI — whatever you call it.

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