LLM SEO: Become the Answer, Not the Echo

AI
Written By Matt Avery

Over the past eleven months, I’ve been trying to answer what sounds like a simple question: How should we change SEO for a world where people increasingly ask AI, not Google, for answers?

The deeper I’ve gone, the less consensus I’ve found.

On one side, there’s a flood of content about using AI tools to produce SEO-optimized content faster. On the other, there’s a newer conversation about optimizing content for AI systems themselves — large language models (LLMs), AI overviews, tools like Perplexity, and so on.

Those are two very different problems.

If you just prompt, “Make this content good for AI search,” you can end up in a feedback loop: AI rewriting AI-inspired content using patterns learned from other AI-inspired content. That’s the race to the bottom. In that world, you’re not the source; you’re the echo.

The more I’ve read, tested, and watched in real deployments, the clearer this has become: “AI SEO” isn’t about hacks. It’s about becoming the answer worth repeating.

There is no secret AI-SEO trick

When you read enough pieces on “SEO for AI” or “LLM SEO,” the split is obvious.

Some authors are honest: it’s early, and much is still unknown. Their advice usually boils down to “keep doing good SEO, plus some technical hygiene so LLMs can crawl you effectively.”

Others are trying to sell a productized “LLM SEO” service. Underneath the branding, most of it is still the same fundamentals: strong content, structured data, entity building, and basic PR — just wrapped in new terminology.

That lines up with what I’m seeing in practice.

If your site is slow, unstructured, thin on content, and hard to crawl, no amount of “AI SEO” tactics will move the needle. You still need:

  • Clear information architecture

  • Useful, original content

  • Clean HTML and sensible on-page SEO

  • Real authority signals across the web

LLMs aren’t magic. They’re trained on the web we already have. If you don’t show up there, you’re not going to magically show up in AI overviews either.

LLM SEO = become the answer

One of the better definitions I’ve seen describes LLM SEO as “the art of becoming the answer.”

That’s a helpful lens, because it forces you to think beyond “rankings” and into concept ownership.

In practical terms, becoming the answer means four things:

  1. You own the topic with depth. Your content doesn’t just skim the surface. It explains trade-offs, use cases, common mistakes, and edge cases. It uses your internal data or experience. It has a point of view. If someone is aggregating the world’s knowledge on that subject, your content deserves to be included.

  2. You structure that depth in a way machines can use. Good headings. Clear sections. FAQs. Summaries. Tables when helpful. Schema markup where it makes sense. The goal is not “pretty blog post,” it’s “clean information object” that models can reason over.

  3. Other people reference you. You’re not the only source saying your brand is credible. You’re mentioned in reviews, Reddit threads, listicles, YouTube videos, and industry publications. LLMs don’t just read your site, they read the consensus around your site.

  4. You keep the important things fresh. High-intent, strategically important content shouldn’t decay. Outdated content is still content — but now it’s training data working against you.

When those four things are true, your odds of being pulled into AI-generated answers go up, even if the “ranking mechanics” of each AI product are still evolving.

Technical reality: LLMs are more picky than Google in one important way

There’s also a technical detail people gloss over.

Google has become very good at handling JavaScript-heavy sites. Many LLM crawlers have not — and in some cases, they simply don’t render JS at all.

That creates a quiet but important gap: if your most important content only appears in the browser after client-side rendering, there’s a good chance some AI crawlers never see it.

So the basic hygiene for “LLM-aware SEO” looks like this:

  • Don’t block legitimate LLM user-agents in robots.txt if you want to be surfaced in AI answers.

  • For critical content, prefer server-side rendering, static generation, or at least ensuring that the core text exists in the initial HTML.

  • Use structured data and clean markup to make meanings explicit.

  • Maintain sitemaps and make them easy to discover.

This isn’t glamorous work, but if you skip it, you’re fighting with one hand tied behind your back.

Queries are becoming paragraphs, not keywords

The other shift is on the demand side.

Traditional search revolved around compressed keyword phrases: “best CRM software,” “ergonomic wireless mouse,” “best mattress for side sleepers.”

AI search is much more conversational and constraint-heavy. A modern query looks like:

“What’s the best CRM for a 50-person marketing team that integrates with HubSpot and stays under $200/month?”

Or in my world:

“I’m new to Airbnb and I don’t want to blow my budget. How should I furnish three bedrooms so guests sleep well but costs stay reasonable?”

That change is huge.

When you’re optimizing only for keyword phrases, it’s easy to hit a ceiling and say, “We’ve already written about everything.” When you optimize for real situations, you quickly realize how much useful content is still unwritten:

  • Guides for specific scenarios

  • Trade-off breakdowns for different budgets

  • Advice by use case (Airbnb host vs growing family vs student housing)

These aren’t just SEO plays. This is demand creation and brand building. And while it isn’t “Amazon SEO” directly, it absolutely supports marketplace performance indirectly. LLMs aggregate everything — your website, reviews, Reddit threads, listicles, and videos — into a picture of your brand and products.

The more nuanced questions you help people answer, the more often you’re a credible candidate when an AI system is deciding what to recommend.

Marketplaces: control the controllable, extend beyond the platform

If you’re selling on a marketplace like Amazon, you’re dealing with extra constraints. You don’t manage robots.txt. You don’t define most of the schema. You can’t change how the platform’s HTML is structured.

That doesn’t mean you’re powerless.

On-platform, you can still:

  • Audit your top sellers and rewrite titles, bullets, and enhanced content so they clearly answer who the product is for, when to use it, and what makes it different.

  • Make sure imagery and content work together to tell a coherent story, not just dump features.

Off-platform, you can:

  • Encourage video reviews and independent write-ups on key SKUs.

  • Show up in listicles, comparisons, and “best for X” content where your products naturally belong.

  • Participate in communities (like Reddit) where real buyers talk about their problems and solutions.

LLMs see all of this. When they generate an answer, they don’t care whether the initial information lived on your DTC site or on a review site — they care whether it’s relevant, credible, and clearly expressed.

Use LLMs themselves as your test bench

The biggest difference between now and the old SEO world is transparency.

Search engines historically treated their ranking algorithms like black boxes. LLMs, by contrast, will often show you the citations they used or even outline their “thinking” process step-by-step.

You can use that to your advantage.

Pick a high-value question you want to win. Run it through several systems: ChatGPT, Claude, Gemini, Perplexity, and others. Run the same prompt every time.

Then study what comes back:

  • Which brands are mentioned repeatedly?

  • Which pages are being cited?

  • Are answers leaning on in-depth guides, short listicles, reviews, or forums?

  • How is the problem framed? Is cost emphasized, quality, simplicity, something else?

Once you’ve done that, ask a harder question: Where are we missing?

Do you lack a definitive guide on that topic? Are there no third-party sites talking about your solution? Is your brand name inconsistent across channels?

Those gaps are your roadmap. This is essentially “answer engine optimization” without waiting for a vendor to tell you what to do.

A realistic way to get started (90–180 days)

If you want to operationalize this without blowing up your roadmap, a phased approach works well.

Phase 1: Fix the basics Audit your core pages for crawlability, indexability, and basic on-page SEO. Make sure your most important content is visible in raw HTML, isn’t blocked, and is linked in your sitemaps. Clean up any obviously thin or duplicate pages that dilute your authority.

Phase 2: Build “definitive” answers for a few key concepts Choose a handful of topics where you really want to be the answer. For each, create or refactor one main page into a true hub:

  • Clear structure and headings

  • FAQs that match how people actually ask questions

  • Embedded data, visuals, or frameworks that only you can provide

  • Sensible internal links to supporting content

Phase 3: Expand your footprint beyond your own site Identify the external places that matter for your audience: review sites, niche publications, relevant subreddits, YouTube creators, etc. Then build a simple plan to show up there over time — not with spam, but with useful contributions, data, or collaboration.

Ongoing: Review your “AI visibility” monthly Run a set of fixed test prompts through multiple AI systems each month. Track whether your brand shows up at all, how it’s described, and which assets get cited. Bring that data into your content and PR planning the same way you’d bring in search console or analytics data.

Resources for deeper reading

If you want to dig into some of the sources that informed this thinking, here are a few worth your time:


Matt Avery
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