LLM SEO Checklist: What to Change on Your Own Website

Quick Answer: For an October 2026 LLM SEO check, make page links crawlable HTML anchors and keep facts visible in structured content. Match structured data to that content. Set OAI-SearchBot and GPTBot rules independently, allow PerplexityBot through robots.txt and firewall IP rules, and allow 24 hours for search adjustments.^1,2,3,4,5

Summary in a mind map

LLM SEO Checklist: What to Change on Your Own Website
│
├─ Page discovery
│   ├─ Name a buyer question and its intended page
│   ├─ Use HTML anchors with href for important routes
│   └─ Verify each link reaches the intended live page
│
├─ Visible information
│   ├─ Answer the question in readable page text
│   ├─ Use descriptive headings and relevant images
│   └─ Match structured data to what readers can see
│
├─ Crawler policy
│   ├─ Decide on OAI-SearchBot and GPTBot separately
│   ├─ Permit PerplexityBot where search access is intended
│   └─ Keep firewall IP rules current
│
└─ Evidence and follow-up
    ├─ Save deployed page and access changes
    ├─ Recheck OpenAI search rules after about 24 hours
    └─ Observe answers without promising placement

Define the page and the question

An LLM SEO implementation checklist is most useful when it names the pages being reviewed. For a commercial lighting distributor, start with one representative product family page that answers a buyer's specific question about choosing a fixture. Record its URL, the question, and the pages that should lead a buyer to it. The record makes a later crawl or content change testable. It also keeps a general discussion of AI search from replacing an examination of the actual website.

The review separates four observations. Can a crawler reach the page? Is key information readable? Does structured data describe it faithfully? Does each access layer permit the named crawler? These are different checks with different owners. A missing internal link is an editing or navigation task. A firewall rule is an infrastructure task. A product description that omits the answer belongs to the content owner. One combined score obscures the next action.^1,2,4,5

The distributor can use a small working list, with one row per page and question. The row cannot promise a citation. Google's guide recommends unique, valuable content and a clear structure for search features; it does not turn a completed checklist into an entitlement to appear in an AI response.³ A useful completion criterion is narrower: the team can show what was checked, what changed, who approved it, and what still requires observation.

Make HTML links crawlable

Google's link guidance recommends an HTML anchor element with an href attribute because its crawlers can reliably extract the destination URL from that form.¹ The reviewer examines the actual link in the rendered page, not merely the words that appear in a design mock-up. A card that looks clickable but has no extractable destination may work for a person using a particular script while failing this straightforward crawlability check.

The distributor's catalogue gives the test a concrete shape. From the room-category page, a relevant fixture comparison should have a real link to the comparison page. From that comparison, a buyer should reach the product detail that contains dimensions and other choice criteria. The record identifies the source and destination of each link. If two visually identical cards behave differently, the audit should point to the specific card, page and HTML element. The page owner can then repair one failing route rather than order an indiscriminate navigation rewrite.

Useful anchor text describes the destination in ordinary language. A buyer selecting office lighting should know whether a link opens a product specification, a comparison, or a general buying guide. The stated crawler requirement is the anchor and its href.¹ The wording decision concerns clarity. It does not promise a special AI-search boost. The work order keeps that distinction so a technical fix can be verified independently from copy changes.

The review covers links from both the site's main navigation and related editorial pages. A product page that appears in a sitemap but has no helpful path from related content can still leave a visitor with an awkward journey. The improvement is not to add links everywhere. Routes belong where a buyer has a reason to continue. The deployed page then needs an anchor that points to the intended live destination. The review record should include both ends of the route, since a syntactically correct link to the wrong product family has not solved the user's problem.

Match markup to visible facts

Google's structured data content and relevance guidelines say that markup must represent a page accurately and describe information visible to readers.² The practical check is a side-by-side comparison. The reviewer opens the rendered page and the structured data, then traces each significant marked-up fact to a visible line, table or product field. If the data names a specification that the page does not display, correct the mismatch. A machine-readable statement does not excuse an absent reader-facing explanation.

For the lighting distributor, a model number, dimension or other product attribute should be the same fact in both places. A page editor may revise a product field while an older structured data value remains in a separate template. The audit therefore needs a named owner for both the page and its markup. Where the rendered text is wrong, fixing only the markup leaves the buyer misinformed. Where the markup is wrong, rewriting otherwise accurate page text merely hides the implementation defect.²

The checklist should not confuse validity with relevance. A markup block may parse correctly yet describe an item other than the one the page shows. Conversely, a clear product page may have no marked-up version of a particular sentence. Google's guideline concerns accurate representation of visible content, so the reviewer should record the specific mismatch, its location, and the approved correction. That is more informative than a general instruction to 'add schema'.²

Structure useful page content

Google's current AI optimisation guide recommends unique, valuable content with well-structured paragraphs, descriptive headings and relevant images.³ Apply that recommendation to the page's job. If the buyer asks how two fixtures differ, the page should make the differences explicit near the relevant products. A heading such as 'Mounting options for this fitting' gives the reader a better route than a generic 'More information' heading. The value lies in the explanation, not in repeating a search phrase.

The main answer belongs in page text. An image may show how a fitting looks in a room, but the page text should also state every dimension or limitation shown in the image. The cited Google guidance values useful content and relevant images.³ Text carries choice criteria. Images provide visual evidence. This is an editorial division of labour, not a claim that a specific layout is a ranking requirement.

Original information should come from what the business can actually substantiate about its products or service. A distributor might explain the conditions under which a fitting can be installed or how two catalogue entries differ. It must have checked those statements against its own material before publication. Copying a broad explanation that could sit on any distributor's page gives the buyer little reason to trust the recommendation. Google describes the desired content as unique and valuable.³ A fashionable acronym alone cannot meet that test.

An editor can use the intended buyer question as a reading test. The editor reads only the first answer paragraph and the section named by the question. Can someone identify the relevant options and their limits without opening another page? If not, name the missing information and find its accountable source inside the business. An invented technical specification cannot make a page complete. The checklist can mark that fact as pending, while the team improves the parts it can verify now.

Google also states that Search ignores LLMS.txt files; maintaining one neither improves nor harms Google visibility or rankings.³ This item deserves its own line because a low-effort file can look like a finished AI-search project. The absence of a Google benefit is a reason to prioritise page content, HTML links and accurate markup first. It is not evidence about how every other service treats such a file, so the website checklist should avoid a universal claim.

Set OpenAI crawler rules

OpenAI distinguishes OAI-SearchBot, which relates to search, from GPTBot, which relates to crawling for foundation-model training. Its documentation says their controls are independent: a website can allow search access and disallow training access.⁴ The implementation checklist needs two explicit policy decisions. A single row labelled 'AI bots' loses the distinction that the site owner is allowed to make.

The desired state should be recorded before changing robots.txt. For each user agent, record whether the site's chosen policy permits the relevant paths. The reviewer then reads the live file as served from the website and compares its rules with the intended state. The reviewer should also note whether the same URL can be reached through ordinary site infrastructure. A robots.txt rule and an infrastructure block are separate layers.^4,5 A correct file cannot clear a firewall restriction.

For the distributor, a product comparison page may be suitable for search discovery while internal planning pages remain outside the public website. The policy question is which public content the business means to expose to each crawler, not whether it can achieve a citation by adding an allow rule. The policy owner receives the exact proposed rule before deployment. Record the deployed version and its time so the team can tell a later policy change from a content edit.

For search results, OpenAI says its systems can take approximately 24 hours to adjust after a robots.txt change.⁴ The change ticket needs a timed follow-up. An immediate check may confirm that the new file is live, but it cannot prove that OpenAI has already applied the new rule. Equally, the approximate interval does not promise a fetch, an index entry or a citation. It tells the operator when to review the observed access state again.

Keep PerplexityBot access current

Perplexity describes PerplexityBot as serving search rather than foundation-model training. Its crawler guidance recommends permitting the bot in robots.txt and allowing its published IP ranges through the web application firewall for search visibility.⁵ Those are two checks. The first is the site's declared crawl policy; the second is the network control that may admit or reject a request despite that declaration.

The firewall review identifies the rule source, update method and responsible person. Perplexity recommends automatic updates as its published IP ranges change.⁵ Its official endpoints are perplexitybot.json for PerplexityBot and perplexity-user.json for Perplexity-User.⁵ A rule copied once into a static allowlist can become stale. The checklist should record where the ranges are obtained and how a change is propagated to the firewall. This is a maintenance control, not a one-time page edit.

If the organisation uses a managed firewall, the person editing website copy may have no access to its configuration. The infrastructure owner handles the check and asks for a dated confirmation of the rule and update mechanism. In a small business, that may be a hosting provider or a contractor. The editorial record should still contain the result because the page's search accessibility depends on that separate layer. Avoid claiming a bot was blocked merely because no answer cited the page; the observed symptom alone does not identify the blocking layer.

The Perplexity documentation and the OpenAI documentation describe different crawlers and controls.^4,5 The implementation sheet keeps their entries separate. A policy that is sensible for one provider should not be copied mechanically to another. The named user agent, purpose, robots.txt policy, firewall decision and review date are the minimum useful fields. The page owner can then understand what a later infrastructure change might affect.

Check the result and its limits

The implementation pass ends with evidence about the website, not a promise about an answer engine. Keep the before-and-after HTML for a repaired link, a page excerpt for a corrected fact, and the deployed robots.txt and firewall decisions. These records show whether the intended work happened. They do not show that a particular generated answer will cite the page. Google's AI guidance speaks to useful content and Google Search; OpenAI and Perplexity describe crawler access. None of those statements provides a universal citation guarantee.^1,2,3,4,5

A small review uses the original buyer questions after the changes. If the page is absent from an observed answer, first confirm which technical conditions have actually been checked. Then inspect the answer itself: perhaps the page does not address the question as clearly as the team believed. The record holds the prompt, date and observed source links so a later comparison uses the same question. Avoid treating a single response as a stable performance rate.

This checklist cannot decide a business's preferred training policy or the truth of its product specifications. Those choices require the business owner and the relevant product expert. Nor does it measure how often an answer system might select the site. It identifies website changes that the cited providers actually document. For a wider explanation of the field, read ChatGPT SEO and generative engine optimisation; use the separate question of whether ChatGPT recommends your business when planning observations.

What to do next

Start with a product or service page and a real customer question. The first check covers its incoming HTML anchors, visible answer and any structured data. The record needs separate OAI-SearchBot and GPTBot decisions; the team then checks PerplexityBot in robots.txt and the firewall. After changes, the team retains the deployed evidence. It reviews OpenAI search access again after roughly 24 hours.^1,2,4,5

If the next task is to check AI mentions of your brand, keep those observations separate from the technical repair log. AI Answer Audit is a possible next step.

In words, the tree branches like this: if an important destination lacks a crawlable HTML link, repair the link first. If the linked page lacks a visible answer or its markup conflicts with that answer, correct the content or markup. If those pass, check OAI-SearchBot, GPTBot and PerplexityBot against the intended policies, including the firewall rule. Record the result and recheck after a timed crawler-policy change.