Generative Engine Optimization for WordPress: The Complete Guide

Generative engine optimization, GEO, is the work of making your content legible, trustworthy, and quotable to the language models that now sit between your site and a growing share of your audience. When someone asks ChatGPT to compare two WooCommerce plugins, asks Perplexity for the best local dentist, or gets a Google AI Overview before they ever see a blue link, a model is reading a handful of sources, deciding which ones are credible, and generating an answer that may or may not name you. GEO is the practice of increasing the odds that it does.

This matters now because the mechanics of discovery have genuinely changed, not just the marketing language around them. AI Overviews appear on a large share of Google queries. ChatGPT, Perplexity, and Claude all browse and cite live web content when they answer questions that require current information. None of this replaced traditional search, but all of it sits on top of it, reading the same pages your SEO work already produces and deciding, in real time, whether those pages are worth quoting.

Most of what has been published about GEO so far is platform-agnostic theory: write clear content, be authoritative, get cited by others. That advice is not wrong, but it is not actionable if you run a WordPress site and want to know what to actually change. WordPress has a specific rendering model, a specific plugin ecosystem, a specific robots.txt file, and specific ways content gets marked up. A generic GEO checklist does not tell you where llms.txt lives on a WordPress install, what your theme's server-side rendering does to a crawler that cannot execute JavaScript, or which of your existing SEO plugin's schema settings actually help an AI system versus just Google's classic rich results.

This guide is the WordPress-specific version. Every tactic below is paired with the actual mechanism: a real llms.txt file you can copy, actual robots.txt bot rules for the crawlers that matter today, and concrete before-and-after examples of how to restructure a paragraph so a model can lift it cleanly. Rankwyn's AEO, AGO, and GEO feature set automates a good portion of this, but the underlying practices apply whether you use Rankwyn, a different plugin, or hand-edit your theme files.

What is GEO? How it differs from AEO and traditional SEO

The short version: SEO is the foundation, AEO is about winning a direct answer, and GEO is about being the source an AI system chooses to cite, paraphrase, or recommend when it generates a longer response. All three overlap heavily and none of them replaces the others.

Traditional SEO is the discipline of getting a page to rank in a list of results: crawlability, indexability, page speed, internal linking, backlinks, keyword relevance, and topical authority. Every technique in AEO and GEO depends on a page that SEO fundamentals already got crawled, indexed, and judged reasonably credible. If a page cannot be crawled, it cannot be cited by a model that relies on retrieval, and if a domain has no established authority signals, a language model has less reason to trust it over a competitor.

Answer engine optimization (AEO) is a narrower, older practice: structuring content so it wins the direct-answer slot, a featured snippet, a People Also Ask box, a voice assistant response. AEO is about matching a specific question to a specific, extractable answer, usually a single paragraph or list, positioned so a search engine's snippet algorithm can lift it verbatim. AEO existed before generative AI and is really an optimization for pattern-matching extraction, not for reasoning.

Generative engine optimization (GEO) is broader and newer. It covers how large language models read, synthesize, paraphrase, and attribute content when generating a novel answer that draws on multiple sources at once, not lifting one snippet but blending several. A model answering "what's the best caching plugin for a high-traffic WooCommerce store" is not looking for one extractable paragraph, it is reading several articles, weighing which claims are corroborated across sources, and constructing a synthesized recommendation, sometimes with citations, sometimes without. GEO is about earning a place in that synthesis: being clear enough to be understood correctly, specific enough to be worth quoting, and credible enough to be trusted alongside or above competing sources.

In practice, the three layer on top of each other. You need SEO fundamentals to get crawled and indexed in the first place, AEO structure to make individual facts extractable, and GEO-specific work, clean formatting, machine-readable metadata, and a presence in the sources models actually pull from, to be one of the two or three domains an AI system decides to mention by name.

It is worth being precise about one more distinction that gets blurred often: GEO is not the same as prompt engineering, and it is not something you can do to a model directly. You have no way to instruct ChatGPT or Perplexity to prefer your site, and any content that reads as an attempt to manipulate a model rather than inform a reader, hidden instructions embedded in a page, for instance, is more likely to be filtered out or ignored than rewarded. GEO works indirectly, by making your actual content the kind of source a retrieval system finds, trusts, and finds useful to quote. The lever you control is the quality and legibility of what you publish, not the model's internal weighting.

How AI crawlers and retrieval systems actually read a WordPress site

Before any content strategy matters, the crawler has to successfully fetch and parse the page. This is where a surprising number of WordPress sites lose the game before it starts, and it is worth understanding the mechanics rather than treating this as a checkbox.

Most AI crawlers, GPTBot, ClaudeBot, PerplexityBot, and the retrieval systems behind ChatGPT's browsing and Perplexity's answers, fetch a URL, read the raw HTML response, and move on. They generally do not execute JavaScript the way a headless Chrome instance for Googlebot's rendering pipeline does. Google's own crawler has a two-wave rendering process where it queues JavaScript-heavy pages for a second rendering pass, sometimes days later. Most AI crawlers do not appear to offer that grace period at all. If your WordPress theme or a page builder plugin renders the actual body content client-side, injecting text into the DOM after the initial HTML loads, an AI crawler that only reads the first response sees an empty shell, or worse, a loading spinner and a handful of skeleton `div` elements.

This is one of the more important, least discussed differences between classic SEO and GEO on WordPress. A site can rank perfectly well in Google because Googlebot eventually renders the JavaScript, while being functionally invisible to an AI crawler that never gets past the first fetch. If you are running a heavily client-rendered page builder, a single-page-app-style theme, or a plugin that lazy-loads main content behind a JavaScript framework, you should specifically check what a plain HTTP request returns, not what a browser shows you.

The practical test is simple: fetch the page with curl or a similar tool that does not execute JavaScript, and read the raw response. If your headline, your body paragraphs, and your key facts are present in that raw HTML, you are in good shape. If they are missing and only appear after client-side rendering, an AI crawler is very likely seeing the same gap.

The second practical factor is speed and size. AI crawlers, like search engines, operate under a crawl budget, they will not wait indefinitely for a slow server or wade through megabytes of unnecessary markup to find the actual content. WordPress sites running unoptimized page builders, dozens of render-blocking scripts, or bloated theme frameworks routinely ship pages where the actual textual content is a tiny fraction of the total payload. Fast, plain, semantically structured HTML, real `article`, `h1` through `h3`, and `p` tags wrapping the actual words, consistently outperforms heavier markup for both crawl efficiency and extraction accuracy. This is one more area where a lean, well-coded WordPress theme and a caching layer are not just a page-speed nicety, they are a GEO requirement.

Caching interacts with this in a way worth calling out separately. A full-page cache that serves the same pre-rendered HTML to every visitor, human or bot, is generally a GEO advantage, since it guarantees a crawler sees exactly the same content a human would, quickly and without waiting on a database query or a PHP render cycle. Problems show up when caching is configured to bypass or vary based on user agent, for instance when a security plugin serves a stripped-down or challenge page specifically to unrecognized bots. That pattern, common on sites that have been targeted by scraper abuse in the past, will silently take an AI crawler out of the loop even though the site looks completely normal to every human tester on the team.

It is also worth checking what happens on a cold cache or under load. Some hosting setups rate-limit or queue requests during traffic spikes, returning a temporary error page instead of the real content. A crawler that hits that error page during a bad moment may not retry for days, depending on its own scheduling. If your site has ever been flagged for slow response times in Search Console, treat that as a signal worth investigating for AI crawlability too, not just for classic SEO health.

llms.txt for WordPress: what it is and how to write one

llms.txt is a proposed convention, formalized at llmstxt.org, for giving language models and AI agents a concise, curated map of a website written specifically for machine consumption rather than for human browsing. It sits at the root of your domain, at yoursite.com/llms.txt, as a plain markdown file. It is not a technical standard enforced by any browser or search engine, and it is not guaranteed to be read by every AI system. It is closer to a well-formed sitemap crossed with an executive summary: a small number of AI-oriented tools already look for it, and providing one costs you almost nothing.

The format itself is deliberately simple. It starts with an H1 containing your site or company name, followed by a short blockquote summary of what the site or business is, then optional free-text context, then one or more H2 sections that group links to your most important pages with a one-line description of each. The convention favors brevity over completeness: this is not meant to be a full sitemap, it is meant to be a curated shortlist of the pages you would hand to a new team member if you had thirty seconds to explain your site.

Here is a realistic llms.txt file for a WordPress-based SaaS marketing site:

# Acme Analytics

> Acme Analytics is a WordPress plugin that gives site owners real-time
> traffic and conversion dashboards without sending data to third parties.
> This file is a machine-readable index of the site for AI assistants
> and language models.

Acme Analytics is self-hosted, GDPR-compliant by default, and installs
like any standard WordPress plugin. It is built for site owners who
want first-party analytics without a separate SaaS dashboard.

## Product

- [Features overview](https://acmeanalytics.com/features): Full list of
  dashboard, tracking, and reporting capabilities.
- [Pricing](https://acmeanalytics.com/pricing): Plan tiers, feature
  comparison, and free plan details.
- [Changelog](https://acmeanalytics.com/changelog): Version history and
  recent updates.

## Documentation

- [Installation guide](https://acmeanalytics.com/docs/install): Step by
  step setup for a new WordPress install.
- [FAQ](https://acmeanalytics.com/docs/faq): Answers to common setup and
  billing questions.

## Company

- [About](https://acmeanalytics.com/about): Company background and team.
- [Contact](https://acmeanalytics.com/contact): Support and sales contact
  channels.

## Optional

- [Blog](https://acmeanalytics.com/blog): Product updates and WordPress
  analytics guides.

Notice what this file is not doing. It does not try to cram every blog post URL into the file, it does not duplicate your XML sitemap, and it does not read like marketing copy. It reads like a terse, factual index, because that is what an AI system parsing it actually wants: a small number of high-signal links with enough context to know which one answers a given question.

For a WordPress site specifically, the file needs to live at the domain root, which means either uploading it directly to your server's web root or generating it dynamically through a plugin that intercepts the /llms.txt request. Static files are simpler to reason about but require you to remember to update them by hand. A dynamically generated file that pulls from your actual post types, categories, and key pages stays accurate automatically as your content changes, which matters more than it sounds like it should, because a stale llms.txt pointing at pages you have since deleted or restructured is arguably worse than having no file at all, since it actively misleads a system trying to trust it. Rankwyn's free plugin generates and serves your llms.txt file automatically, regenerating it as your content changes rather than leaving it as a one-time export that quietly goes stale.

Keeping it current is mostly a matter of scope discipline. Review the file whenever you add a major new product line, restructure your primary navigation, or retire a page you had listed. You do not need to touch it every time you publish a blog post, the file is meant to represent your site's structure and highest-value pages, not act as a running content feed.

A common mistake on WordPress sites that already have a large content library is trying to list everything. A blog with eight hundred posts does not need eight hundred links in llms.txt, it needs the twenty or thirty pages that best represent what the site actually does: the core product pages, the most authoritative guides, the pricing and comparison pages people actually ask AI assistants about. If you have distinct content categories that serve very different audiences, for instance a documentation section and a marketing blog, it is reasonable to split those into separate H2 groups within the same file, or in some cases to publish a secondary, more detailed file linked from the main one, following the same convention the llms.txt spec describes for an optional llms-full.txt variant aimed at more exhaustive machine consumption.

One more detail worth getting right: serve the file as plain text with a text/markdown or text/plain content type, not wrapped in your theme's HTML template. If a request to /llms.txt returns a full WordPress page, complete with navigation menus, cookie banners, and footer widgets, wrapped around the markdown content, you have defeated the purpose of the format, which exists specifically to avoid making a model wade through page chrome to find the actual information.

Structuring WordPress content so AI systems can extract it cleanly

This is the section that does the most day-to-day work in GEO, and it is also the one most people get subtly wrong, because writing for extraction is a different skill than writing for narrative flow. Language models pulling facts out of a page favor content that states a claim plainly near the top of a section, then supports it, over content that builds up to a conclusion through several paragraphs of context first.

Lead with a direct answer, then explain

The single highest-leverage structural change you can make is to open each major section with a 40 to 60 word direct answer to the question that section is implicitly answering, before you get into nuance, caveats, or supporting detail. This is not new advice, it is essentially what AEO has recommended for featured snippets for years, but it matters even more for GEO because a model synthesizing an answer across several sources is scanning for the clearest, most self-contained statement of fact, and it is more likely to lift or paraphrase a sentence that already reads like an answer than one buried inside a longer argument.

Here is the difference in practice, using a real example of the kind of paragraph found on thousands of WordPress plugin pages.

The well-structured version states a specific, checkable claim in the first sentence, then backs it up. A model extracting facts for a synthesized answer can quote or paraphrase that opening sentence with confidence that it is not stripping away necessary context, because the sentence was written to stand alone.

Use comparison tables and structured lists where they genuinely fit

Tables and lists are disproportionately easy for a model to parse correctly compared to the equivalent information embedded in prose, because the structure itself encodes the relationships: this row is one option, this column is one attribute, this list item is one step. When you are genuinely comparing two or more things, plugins, plans, methods, use an actual table rather than a paragraph that says "Option A has X while Option B instead offers Y, though Option C is different again." The prose version forces a model to reconstruct the comparison from unstructured text, which increases the odds of an error creeping into a generated summary, an error that then gets attributed back to you as the source.

FAQ blocks and matching schema

A well-written FAQ section serves double duty. As visible content, it gives you natural answer-first paragraphs organized around the actual questions your audience asks. Marked up with FAQPage schema, it also gives crawlers and AI systems a machine-readable version of the same question-and-answer pairs, which several AI systems have shown a preference for when selecting which page to cite for a specific query. On WordPress, this generally means using your SEO plugin's FAQ block or a matching Gutenberg block that outputs both the visible HTML and the corresponding JSON-LD schema from the same content, rather than writing an FAQ section as freeform text and hoping a plugin infers structure from it. Keep each answer genuinely short, two to four sentences, matching the same answer-first discipline as the rest of the page.

Corroboration matters more than most site owners expect

One of the more counterintuitive findings in GEO research is how heavily AI systems weigh third-party corroboration over what a brand says about itself. A claim that only ever appears on your own domain, no matter how well formatted, carries less weight than the same claim echoed independently by a review site, a forum thread, an industry publication, or another blog. This is not something you can fix purely through on-page formatting, it is closer to a digital PR and outreach problem: getting your product mentioned, reviewed, and linked from sources you do not control. If you already run a link-building or PR program for classic SEO, that same work is doing double duty for GEO, since the same third-party mentions that build domain authority for search rankings are also the corroborating evidence AI systems look for before trusting a claim enough to repeat it.

In practice this means a WordPress site owner working on GEO should not treat it as a purely technical, on-site project. Submitting your plugin or product to relevant WordPress directories, getting reviewed on independent WordPress blogs, answering questions on forums like the WordPress.org support forums or Reddit's WordPress communities with your real site linked where appropriate, and being mentioned in roundup or comparison posts written by other people all contribute to the corroboration signal that makes an AI system more willing to cite you as a primary source rather than just acknowledging you exist.

Auditing and fixing AI bot access on WordPress

None of the content work above matters if the relevant crawlers cannot reach your pages in the first place. A meaningful number of WordPress sites, often without the owner realizing it, are running security plugins, firewall rules, or a robots.txt file inherited from a theme or hosting default that blocks AI crawlers outright, sometimes as a blanket "block all bots except known search engines" rule that was written before AI crawlers existed as a named category.

Start by identifying which bots are relevant to you. This is not an exhaustive list of every crawler on the internet, it is the set that matters most for GEO specifically, because each one feeds a different AI product's ability to browse, retrieve, or train on your content.

To check your current state, look at your live robots.txt file at yoursite.com/robots.txt in a plain browser or with curl, and also check whether a security plugin or your host's WAF is silently blocking these user agents at the server level, since a permissive robots.txt does not help if a firewall rule returns a 403 before the request ever reaches WordPress. Many WordPress security plugins ship with bot-blocking presets that were written to stop scrapers and spam bots and end up catching AI crawlers in the same net.

A reasonable, permissive robots.txt for GEO purposes looks like this. This explicitly allows the crawlers discussed above while leaving your existing rules for other bots untouched:

# Allow AI crawlers relevant to generative engine optimization
User-agent: GPTBot
Allow: /

User-agent: ChatGPT-User
Allow: /

User-agent: OAI-SearchBot
Allow: /

User-agent: ClaudeBot
Allow: /

User-agent: PerplexityBot
Allow: /

User-agent: Google-Extended
Allow: /

User-agent: Bingbot
Allow: /

User-agent: CCBot
Allow: /

# Default rule for everything else
User-agent: *
Disallow: /wp-admin/
Allow: /wp-admin/admin-ajax.php

Sitemap: https://yoursite.com/sitemap.xml

Blocking these crawlers is a legitimate choice for some site owners, particularly around GPTBot and CCBot, which are primarily about training-data collection rather than live retrieval, and some publishers block them specifically to withhold training rights while still allowing live-browsing bots like ChatGPT-User. That is a defensible content-licensing decision. What is usually not intentional is blocking ChatGPT-User, PerplexityBot, or Google-Extended, since blocking those specifically prevents you from being cited in the live, user-facing answers that GEO is trying to win, as opposed to just opting out of training data collection. Know which category you are blocking and why before you set the rule. For a deeper look at AI crawler traffic specifically, including how to tell which of these bots are actually visiting your WordPress site right now, see our dedicated guide on AI crawler traffic for WordPress. Rankwyn's AI traffic insights feature shows this directly in your dashboard rather than requiring you to parse raw server logs.

A robots.txt edit alone is not always enough to confirm access. Many WordPress hosts, particularly managed hosting providers, run their own edge-level bot management in front of your site that never touches your robots.txt file or your WordPress installation at all. If your host offers a firewall dashboard, a bot-fighting mode, or a "block AI scrapers" toggle, check it directly rather than assuming your robots.txt rules are the final word. It is common for a site owner to correctly configure robots.txt, confirm it looks right in a browser, and still be invisible to AI crawlers because a host-level rule intercepted the request before WordPress ever saw it.

The same caution applies to WordPress security plugins with reputation-based blocking. Some of these maintain their own crawler allowlists that lag behind the actual crawler landscape, meaning a plugin last updated before a given AI crawler existed may default to blocking it as an unrecognized bot. Check your security plugin's changelog and settings specifically for AI crawler handling, and do not assume a plugin marketed as SEO-friendly automatically accounts for the newer AI-specific user agents.

Schema types that help AI systems cite you correctly

Structured data was originally built for search engine rich results, but the same JSON-LD markup doubles as a machine-readable summary that AI systems can parse with far less ambiguity than free-form HTML. This section covers only the types most relevant to GEO specifically at a summary level; for full implementation detail on all schema types, see the complete schema markup guide for WordPress.

Organization schema establishes who is actually publishing the content, your legal or brand name, logo, and official site, which matters when a model is trying to determine whether a claim comes from a credible, identifiable entity or an anonymous page. This is foundational trust signaling more than a citation trigger on its own.

WebSite schema identifies the site as a coherent entity and, when paired with a sitelinks search box property, can help a model or search feature understand that your site has internal search and structured navigation, reinforcing that it is a maintained, real property rather than a static one-off page.

Article schema (or its more specific variants like BlogPosting) attaches an author, a publish date, and a modified date to a piece of content, which matters directly for GEO because freshness and clear authorship are both factors AI systems weigh when deciding whether a claim is current and attributable. An article with no visible date or author is harder for a model to treat as authoritative, since it cannot tell if the information is five years stale.

FAQPage schema marks up your question-and-answer pairs in a format that both search engines and AI systems can parse directly without inferring structure from prose, and it is one of the more consistently cited schema types in AI-generated answers precisely because the question-answer format already matches how users phrase queries to a model.

HowTo schema does the same for step-by-step instructions, marking discrete numbered steps so a model answering "how do I do X" can extract a clean sequence rather than parsing it out of a narrative paragraph. This is particularly relevant for WordPress tutorial and documentation content, where the underlying content is naturally a sequence of steps.

Rankwyn's schema and structured data feature covers all five of these plus the rest of the schema.org types relevant to a typical WordPress site, generating the markup automatically from content you have already written rather than requiring you to hand-author JSON-LD.

A practical note specific to WordPress: if you are running more than one SEO or schema plugin at once, a genuinely common setup on sites that have changed plugins over the years without fully removing the old one, check for duplicate or conflicting JSON-LD output on the same page. Two competing Article or Organization schema blocks describing the same page with slightly different data is worse than having only one, since it forces any system parsing the page to guess which block is authoritative, and some validators will flag the page as having invalid structured data entirely. View source on a handful of key pages and count how many `application/ld+json` blocks appear before assuming your schema setup is clean.

Measuring whether your GEO work is actually working, without paid tools

There is no widely adopted, reliable analytics platform yet that tells you your exact citation rate across every AI system the way Google Search Console tells you your ranking position. Server-side referral data from AI chat products is limited, inconsistent between platforms, and often absent entirely because many AI answers do not click through to your site at all, the value is delivered inside the chat interface itself, a phenomenon usually called a zero-click AI citation. That does not mean measurement is impossible, it means you need a manual process instead of a dashboard.

The most reliable DIY method is a monthly manual audit. Build a list of your twenty highest-value queries, the questions your actual customers ask that your content is meant to answer, mixing branded and non-branded phrasing. Once a month, run each query against ChatGPT (with browsing or search enabled), Perplexity, and a Google search that triggers an AI Overview, and log three things for each: whether your domain is cited or named at all, whether the citation is accurate to what your page actually says, and which competing domains got cited instead. Keep this in a simple spreadsheet with a date column so you can track the trend over months rather than treating any single check as definitive, since AI-generated answers are not fully deterministic and can vary between runs of the same query.

This manual process surfaces two kinds of useful signal. First, whether you are being cited at all, which tells you if your GEO fundamentals, crawlability, structure, schema, are working. Second, and just as valuable, whether the citation is accurate. It is common to find that a model is citing you but slightly misrepresenting what you said, which is usually a sign your source paragraph was ambiguous or buried context the model dropped when summarizing, exactly the kind of issue answer-first restructuring fixes.

A secondary, lower-effort check is watching your server logs or your AI traffic dashboard for crawler visits from GPTBot, ClaudeBot, PerplexityBot, and ChatGPT-User. A rising trend in AI crawler visits does not guarantee citations, but it is a leading indicator that these systems are at least discovering and reading your content, which has to happen before a citation can. Rankwyn surfaces this automatically through AI traffic insights, so you are not stuck grepping raw access logs by hand every month.

It helps to separate three distinct signals when you review your monthly audit, since conflating them leads to the wrong conclusion. Crawler visits without citations usually mean discovery is working but your content is not yet winning the comparison against competing sources, which points back to structure, corroboration, or depth. Citations with inaccuracies usually mean discovery and trust are both working but your source content is ambiguous or buried, which points back to the answer-first restructuring covered earlier. And no crawler visits at all, after confirming robots.txt and your firewall are not blocking anything, usually means the page has not been discovered yet, which is a linking and sitemap problem more than a content-quality one. Treat each of these as a different bug with a different fix rather than lumping them together as "GEO isn't working."

It is also worth logging competitor citations alongside your own, not just as a scoreboard but as a research tool. When a competing domain gets cited for a query you expected to win, open that page and read it the way a model would: is the answer stated more plainly, is the claim backed by a source you are not citing, is the page simply newer or more specific than yours. This kind of direct comparison, run consistently over a few months, tends to surface concrete, fixable gaps far faster than abstract GEO advice does.

Set expectations honestly here: GEO is slower to show measurable movement than classic SEO in most cases, because it depends on AI systems re-crawling and re-indexing your content on their own schedules, which are opaque and vary by provider. A meaningful change to your content structure, schema, or llms.txt file can take anywhere from a few weeks to a couple of months to show up in citation behavior, and some AI systems appear to update their retrieval indexes far less frequently than a traditional search engine. Treat the monthly audit as a trend line, not a weekly scoreboard.

The practical WordPress GEO checklist

Use this as a working audit list, not a one-time task. Revisit it quarterly, since crawler rules, schema recommendations, and AI product behavior all continue to shift.

1. 1Confirm your actual body content is present in the raw HTML response (test with curl, not just a browser) rather than injected client-side after page load.
2. 2Publish an llms.txt file at your domain root with a clear summary and links to your most important pages, kept current as your structure changes.
3. 3Check your live robots.txt file and any security plugin or WAF rules for accidental blocks on GPTBot, ChatGPT-User, ClaudeBot, PerplexityBot, and Google-Extended.
4. 4Decide deliberately whether to allow or block training-focused crawlers like GPTBot and CCBot, separately from live-browsing bots like ChatGPT-User and PerplexityBot.
5. 5Rewrite key section openings to state a direct, 40 to 60 word answer before moving into supporting detail.
6. 6Convert genuine comparisons (plugins, plans, methods) from prose into real HTML tables.
7. 7Add an FAQ section with matching FAQPage schema to your highest-traffic pages, keeping each answer to two to four sentences.
8. 8Add HowTo schema to any genuinely step-by-step tutorial or documentation content.
9. 9Verify Organization and WebSite schema are present sitewide, not just on the homepage.
10. 10Add or confirm Article schema with visible author and publish/modified dates on all long-form content.
11. 11Audit page speed and markup weight specifically for crawl efficiency, not just Core Web Vitals scores.
12. 12Remove or simplify JavaScript-dependent rendering for any content you want AI systems to read.
13. 13Build a list of 15 to 20 real customer queries and run a monthly manual citation check across ChatGPT, Perplexity, and Google AI Overviews.
14. 14Track AI crawler visits (GPTBot, ClaudeBot, PerplexityBot, ChatGPT-User) over time to confirm discovery is happening before expecting citations.
15. 15Review and update llms.txt whenever you add a major product line, restructure navigation, or retire a listed page.
16. 16Check that your XML sitemap is complete and referenced from robots.txt, since it still underlies how most crawlers, AI included, discover new URLs.
17. 17Avoid vague, unattributed claims ('many experts agree', 'studies show') that a model cannot verify or attribute, replace them with specific, checkable statements.
18. 18Re-run the raw-HTML content check after any theme, page builder, or major plugin update, since these frequently change rendering behavior without warning.

Where to focus first

If you are starting from nothing, the highest-leverage order is roughly: confirm your content is actually crawlable in raw HTML, fix any accidental AI bot blocks in robots.txt or your security plugin, publish a real llms.txt file, then work through your highest-traffic pages restructuring key sections to lead with a direct answer and adding FAQ schema. Everything else in the checklist compounds on top of that foundation. GEO is not a separate discipline from good WordPress SEO practice, it is the same discipline extended to a new class of reader that happens to be a model instead of a person, and it rewards exactly the things good technical SEO already rewarded: fast, accessible, clearly structured, honestly written content.