MB Web

90 Day LLM SEO Plan for UK SMEs to Win AI Citations

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LLM SEO is the practice of structuring your content, your schema, and your site’s technical foundations so large language models can find, extract, and cite it as an answer. It merges answer engine optimisation (AEO) with traditional SEO groundwork. A highly effective approach is to select a high-value page, rewrite its core answer into a short, extractable block, and ensure it is indexed.

TL;DR:

  • Prioritize publishing clean, extractable answer blocks on high-ranking pages and fix crawl access before content quality to maximize AI citation chances.
  • Focus on RAG optimization by ensuring answer blocks are visible, structured, and easily retrievable across platforms like Perplexity and Google AI Overviews.
  • Use schema markup such as FAQPage and Organization, and keep metadata, URLs, and entity references consistent to enhance AI understanding and trust signals.
  • Measure success through mention rate, citation rate, and share of model metrics across platforms, building a prompt test set that mirrors buyer questions monthly.
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What is LLM SEO and how does it relate to AEO and GEO?

LLM SEO covers everything you do to make a large language model, whether that’s ChatGPT, Perplexity, or Google’s AI Overviews, pull your content into its answers and credit you as the source. You’ll see it used almost interchangeably with answer engine optimisation (AEO) and generative engine optimisation (GEO). In practice, AEO tends to describe the content technique (writing for direct answers), while GEO describes the broader discipline of optimising for generative platforms. LLM SEO is the umbrella most marketers now reach for.

The overlap with traditional SEO is real, but the differences matter. Traditional SEO chases rankings and clicks; AEO chases being the answer itself, often without a click at all. That changes what “success” looks like and what you optimise for:

  • Unit of value: traditional SEO rewards a ranking position; LLM SEO rewards being quoted or paraphrased inside a generated answer.
  • Structure: traditional SEO tolerates long, scannable pages; LLM SEO rewards short, self-contained answer blocks near the top of the page.
  • Signals: traditional SEO leans on backlinks and on-page keywords; LLM SEO leans more heavily on entity clarity, structured data, and third-party consensus about who you are.

Here’s the honest bit most guides skip: you don’t get to choose one over the other. A page needs to rank well enough to be crawled and trusted, and it needs an answer-first block clean enough for a model to lift. Favour short extractable answers for anything transactional or definitional (“what is”, “how much does”, “which is best”). Keep long-form depth for pages meant to build topical authority and earn the backlinks that support everything else. Both jobs still need doing, just for different pages.

How do LLMs choose and cite their sources?

Every citation an LLM produces comes from one of two places: parametric knowledge baked into the model during training, or retrieval-augmented generation (RAG), where the model searches live content and pulls from it in real time. Understanding which pathway you’re optimising for changes your entire strategy.

Parametric knowledge is slow to shift. It reflects whatever the model absorbed during training, which could be months or years old, and you can’t force your way in with a well-optimised page published last week. Building a presence here means sustained brand mentions, consistent coverage, and enough third-party corroboration that you eventually become part of the “common knowledge” the next training run absorbs.

RAG is where the fast wins live. When a model retrieves your page live and quotes it, it’s rewarding extractability rather than history: can it find a clean 40 to 60 word passage that directly answers the query, sitting on a page it can crawl and index right now?

Platform behaviour differs sharply. Perplexity retrieves live content for nearly every query it answers, ChatGPT frequently answers from parametric memory alone, and Google’s AI Overviews lean on Google’s own index. A page invisible to Perplexity might already be baked into ChatGPT’s training data, and vice versa.

The practical takeaway: prioritise RAG optimisation first. It’s the lever you can pull today.

  • Publish clean, extractable answer blocks on pages that already rank reasonably well.
  • Fix crawl access and indexation before touching content quality.
  • Treat parametric presence (media coverage, forums, Wikipedia) as a slower, parallel campaign, not a quarterly sprint.

Practical checklist: 12 ways to increase your AI citation odds

None of this requires a rebuild. Most of it is editing discipline plus a handful of technical fixes.

  1. Write the answer first. Put a 40 to 60 word, self-contained answer to the page’s core question in the first 100 words. No throat-clearing, no scene-setting.
  2. Make it quotable, not clever. State the fact plainly. Models paraphrase clean prose far more reliably than marketing flourish.
  3. Use FAQ schema to mark up the short answer on the page. If your on-page copy and your FAQPage markup say the same thing in the same words, you’ve doubled your extraction surface.
  4. Use visible lists and tables that mirror your JSON-LD. A model reading rendered HTML should see the same structure your schema promises, not a mismatch.
  5. Build genuine third-party mentions. Being referenced on trade press, directories, or partner sites feeds both parametric training data and RAG-era trust signals.
  6. Tidy your Wikipedia and Wikidata presence if you have one, or at minimum keep your Google Knowledge Panel and business listings consistent.
  7. Check crawl access for AI bots. Confirm your robots.txt explicitly allows GPTBot, PerplexityBot, and Google-Extended unless you have a deliberate reason to block them.
  8. Submit new and updated URLs via IndexNow and Google Search Console. Don’t wait for a crawl cycle you can’t control.
  9. Use consistent entity language. If you call yourself “MB Web” on one page and “Michael Bell Web Design” on another, you’re fragmenting your own authority.
  10. Add sameAs links in your Organisation schema pointing to verified social and directory profiles, so models can cross-reference who you actually are.
  11. Keep publish dates and statistics current. A page timestamped three years ago with stale figures gets deprioritised against a fresher competitor.
  12. Audit descriptive URL slugs. Pages with clear, descriptive slugs tend to have better citation rates than generic ones, and it costs nothing to fix on a new page.

Pro Tip: Don’t rewrite every page on your site at once. Pick your three highest-intent pages, the ones already answering a question people actually type into ChatGPT or Perplexity, and get those extractable first. Momentum beats breadth here.

Technical implementation: schema, extractable passages and indexing

Web Design Sussex | 1789328091513 Schema passages and indexing structure 2

An extractable passage isn’t a paragraph you hope reads well. It’s a deliberately engineered block: one clear claim, 40 to 60 words, no pronouns referring back to a previous sentence, and no dependency on surrounding context to make sense. Write it so it could be lifted whole and dropped into someone else’s answer without confusion.

Your JSON-LD needs to match what’s visibly on the page. Schema markup describing an FAQ that doesn’t appear as visible text is a trust signal working against you, since structure alone doesn’t help without genuine authority behind it. Use Article schema for editorial content, FAQPage for genuine Q&A sections, and Organization schema with complete sameAs links on every page that needs entity recognition.

  • Give every page a descriptive, human-readable URL slug that states the topic plainly.
  • Set canonical tags correctly, especially on paginated or filtered content, so retrieval systems don’t index duplicates.
  • Confirm robots.txt doesn’t block the crawlers your citation strategy depends on.
  • Submit sitemap changes through IndexNow for near-instant crawl requests, and cross-check indexation status in Google Search Console weekly during an active push.

Pro Tip: Add visible author bylines with a short credential line, and back them with structured author markup. Structured author markup correlates with higher citation rates, likely because it gives retrieval systems a stronger authority signal to lean on. For more on why that trust signal matters, our piece on E-E-A-T and trustworthiness goes into the mechanics.

How do you measure LLM SEO performance?

Forget keyword rank tracking. The unit of measurement here is the prompt, not the keyword, which means your first job is building a representative prompt test set.

Assemble 30 to 50 prompts that mirror how real buyers phrase questions at each stage: “what is X”, “best X for Y”, “X vs Z”, “how much does X cost”. Run them monthly against the platforms your audience actually uses.

Three metrics matter:

  • Mention rate: the percentage of prompts where your brand or content appears anywhere in the answer, cited or not.
  • Citation rate: the percentage where you’re explicitly linked or named as the source.
  • Share of model: a position-weighted score across your full prompt set that rewards being cited first or prominently, not just mentioned somewhere in the response.

Track these per platform, since ChatGPT’s largely parametric answers behave very differently to Perplexity’s live retrieval or Google’s AI Overviews, and a win on one doesn’t guarantee movement on another. Most teams start manually, running prompts by hand and logging results in a spreadsheet, before graduating to dedicated tracking tools once the process proves its worth. Our audit framework gives you a starting structure for building that prompt set from scratch.

Your 90-day roadmap to better AI visibility

You don’t need a year-long strategy document. You need three focused months.

  1. Month one: inventory and quick wins. Audit your top pages for indexation problems, robots.txt blocks, and missing schema. Pick three high-intent pages and rewrite their opening sections into extractable answer blocks.
  2. Month two: structure and outside signals. Roll out FAQPage and Organization schema across priority pages. Start outreach for genuine third-party mentions and clean up inconsistent business listings, directories, and your Knowledge Panel.
  3. Month three: measure and scale. Run your prompt test set across ChatGPT, Perplexity, and Google AI Overviews. Identify which page templates are actually getting cited, then apply that template to the next tier of pages.

The recommended sequence, fix technical fundamentals first, front-load answers and schema second, then chase third-party consensus third, holds whether you’re doing this in-house or briefing an agency. Skipping straight to outreach before your indexation is sound just wastes the effort.

Why MB Web’s experience shapes this guide

We’ve spent 50 years working with local and national brands on the web design, development, and SEO problems that sit underneath everything covered here. That history matters for LLM SEO specifically, because none of the tactics above work on a site with poor technical foundations. Extractable content on an unindexed page is invisible regardless of how well it’s written.

The agency works with estate agents, charities, industrial businesses, and SMEs in the South East, performing technical audits, building schema and content templates, and managing SEO campaigns that improve page crawlability and competitiveness. When we’re engaged on AI search optimisation work, we apply the same mention rate, citation rate, and share of model framework outlined above. It isn’t a separate methodology dressed up for a new buzzword. It’s the same measurement discipline, pointed at a newer set of surfaces.

Web Design Sussex | 1789328171450 Why MB Web s experience shapes this guide overview diagram 1

Where LLM SEO is heading, and how much to invest

RAG-first work should get the bulk of your near-term budget. It’s the only lever with a feedback loop fast enough to prove itself within a quarter, and most SMEs simply can’t afford to wait a year for parametric presence to build on its own. That said, don’t abandon the slower game. Brand mentions, press coverage, and consistent entity signals compound, and the businesses that show up in ChatGPT’s training data next year are the ones seeding it now.

For a typical SME, we’d suggest putting roughly three-quarters of effort into extractability and technical fixes, and the remainder into sustained off-site presence. Revisit that split every couple of quarters as the platforms themselves keep shifting.

— James Golding

This agency offers a comprehensive AI search optimisation service under one roof: technical audits to identify indexation blocks; website design and development to fix template and schema issues; and SEO campaigns that develop off-site authority over time.

A typical engagement starts with an audit of your priority pages, moves into implementation of schema, extractable content, and crawl fixes, then settles into monthly measurement against a prompt set built around your actual buyers. If your site needs structural changes before any of this content work can land, our website development team handles that alongside the SEO work, so nothing stalls waiting on a developer queue. For a broader look at how we approach this discipline, our AI search service page sets out what’s included.

If you’d like an honest read on where your site currently stands with AI citation, without a sales pitch attached, get in touch about SEO for your website and we’ll talk you through what we’d prioritise first.

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FAQ

What Is LLM SEO?

LLM SEO is the practice of structuring content and technical signals so large language models can find, extract, and cite it in their generated answers, blending answer-first content design with traditional SEO fundamentals like indexation and crawl access.

What’s The Difference Between LLM SEO And Traditional SEO?

Traditional SEO optimises for rankings and clicks on a results page, while LLM SEO optimises for being quoted or paraphrased inside an AI-generated answer, often without any click at all.

Which LLM Is Best To Optimise For?

There’s no single best platform, since Perplexity relies heavily on live retrieval, ChatGPT often answers from parametric training data, and Google AI Overviews draw on Google’s own index, so a genuine strategy covers all three rather than picking a favourite.

Is Traditional SEO Becoming Obsolete Because Of AI Search?

No. Traditional SEO fundamentals, indexation, site authority, and technical health, remain the foundation LLM SEO builds on; without them, even the best-written extractable content stays invisible to retrieval systems.

How Do I Start Measuring My AI Search Visibility?

Build a prompt test set of 30 to 50 realistic buyer questions and track mention rate, citation rate, and share of model monthly across the platforms your audience actually uses, rather than relying on traditional keyword rank tracking.