The GEO Checklist: 25 Things to Fix First

The 25 GEO fixes that matter first fall into four groups: the prompts you target, how extractable and well-evidenced your pages are, the trust, entity and technical signals that let AI systems read and believe them, and how you earn and measure citations. Each fix can be audited as a pass or fail. Buyers now ask ChatGPT, Perplexity, Gemini and Google AI Overviews for shortlists before they reach your site, and a page can rank in the top three and still never be quoted. This generative engine optimization checklist is written for teams that already run SEO and paid programs. It reflects how answer engines retrieve and cite sources in October 2026. Every item has a validation step and a fix-first score, so you know which gaps to close this quarter and which can wait.

What generative engine optimization is and how AI engines choose sources

Generative engine optimization (GEO) is the practice of adapting content so generative AI systems surface, summarize and cite it in their answers. The definition on Wikipedia frames it the same way: visibility inside AI-generated responses as well as in a list of links. GEO builds on SEO rather than replacing it, and the overlap is larger than most vendors admit. The full breakdown of where GEO and SEO split is in a separate article. The short version:

The six retrieval paths into an AI answer

Every check in this list strengthens at least one of these paths:

• Search index: Google AI Overviews draw on Google's index, and Microsoft Copilot draws on Bing's. An unindexed page is not a candidate.
• Live fetching: agents such as ChatGPT-User and Perplexity-User fetch pages on demand when a user asks a question.
• Training data: crawlers such as GPTBot and ClaudeBot collect text that shapes what models "know" about your brand between retrievals.
• Third-party citations: Reddit threads, review sites and comparison articles often answer "best" and "vs" prompts instead of brand sites.
• Entity associations: knowledge graphs and repeated co-mentions tie your brand to a category, a problem and a set of competitors.
• Structured extraction: headings, tables, lists and schema let a model lift a clean passage without rewriting it.

How to use this checklist

Run each check against one priority page or one priority prompt cluster. Mark it pass or fail using its "Pass" line rather than a gut feel, then score the failures with the fix-first model in the last section. If you need the underlying method first, the step-by-step GEO walkthrough covers it from scratch.

Strategy checks: prompts, intent and entities (1–5)

The generative engine optimization strategies to prioritize first decide which prompts you compete for and what AI engines currently reward for them. Every later fix depends on that map.

1. A prompt inventory sorted by query class

Keyword lists miss how buyers phrase questions to assistants. Build a sheet of real prompts in seven classes:

1. Definition: "what is CSPM"
2. Comparison: "Wiz vs Orca"
3. Recommendation: "which tool should a 500-person fintech use"
4. "Best": "best cloud security platform for AWS"
5. Local: "SOC 2 auditors in Austin"
6. Troubleshooting: "why is my agent missing alerts"
7. Multi-step research: "compare three options, then estimate cost"

Pull phrasing from sales calls, Reddit threads, People Also Ask and site search. Pass: for example, 30–60 prompts per product line, each tagged by class and funnel stage. Fail: a list made only of head keywords.

2. A reverse-engineered map of current AI answers

Run every priority prompt in ChatGPT, Perplexity, Gemini and Google (AI Overview plus organic). Record which domains are cited, which brands are named, the answer format (list, table, prose) and which passages appear lifted almost word for word. Pass: a citation map showing the cited source types for each prompt: your site, competitor, Reddit, review site or media. Common mistake: running prompts once. Answers vary between runs, so sample each prompt at least three times.

3. One page mapped to each intent

Assign each prompt cluster to a single target URL. Comparison prompts need a comparison page, troubleshooting prompts need a support-style article, and a product page should not be stretched to cover all of them. Pass: no orphan clusters and no two URLs targeting the same intent. Fail: the homepage is the only candidate for "best" prompts.

4. A defined entity and fixed terminology

Models link brands to categories through repeated, consistent statements. Write one canonical sentence, "[Brand] is a [category] for [audience] that [core job]", and use it on the homepage, About page, product pages and external profiles. Pick one term per concept ("cloud security posture management", not three variants). Pass: the same category sentence appears on your site, LinkedIn, G2 and press boilerplate.

5. A hub-and-spoke topic architecture

Plan one hub page per category that defines the topic and links to spokes: comparisons, use cases, how-tos and glossary entries. This structure shows crawlers and models that you cover the topic in depth rather than in passing. Pass: every spoke links back to its hub, and the hub links to every spoke.

Content and evidence checks: making pages quotable (6–12)

Content passes GEO checks when a model can lift any section as a standalone, accurate, well-supported answer without rewriting it.

6. Answer-first openings

Match the H1 to the prompt's phrasing, and make the first 40–60 words under each heading answer that heading directly. Context and caveats come after the answer. Pass: reading only the first sentence of each section still gives a correct answer.

7. Self-contained passages

Retrieval systems split pages into chunks, so a passage that opens "As mentioned above, it…" loses its subject once extracted. Restate the subject in each section and avoid pronouns that point across headings. Pass: any H3 section pasted into a blank document still makes sense.

8. Definitions and quote-worthy statements

Write one-sentence definitions in the "X is Y that does Z" pattern, plus clear claims that stand on their own ("Agentless scanning covers workloads without installing software on each host"). Fail: definitions buried inside a 200-word paragraph.

9. Tables, lists and summary boxes built for extraction

Use comparison tables with the same attributes in every row (price model, deployment, integrations, best fit), numbered lists for steps and a two- to four-line summary box near the top. Pass: each table has a header row and no merged cells. Common mistake: tables rendered as images, which text extractors cannot read.

10. Original evidence

Pages that add something new get cited over pages that restate the consensus. Add a mini study (for example, "we analyzed 200 support tickets"), named expert quotes, annotated screenshots, worked examples with figures or a scoring framework of your own. Pass: at least one fact or framework on the page exists nowhere else.

11. Fair comparison and "best" pages

Models favor pages that state their criteria and name competitors honestly. List the evaluation criteria, include rivals with their real strengths and say who each option suits. Fail: a "best tools" page that lists only your product.

12. Question-led sections on the page

Short Q&A blocks built from the prompt inventory (check 1) match how assistants phrase sub-questions. Keep each answer to two to four sentences. Pass: the questions come from real buyer phrasing, not invented filler.

Weak vs strong execution

These generative engine optimization examples show what a rewrite changes:

Trust, entity and technical checks (13–19)

Trust and technical checks decide whether an AI system can reach a page, parse it and treat it as a credible source.

13. Named author with credentials and an editorial policy

Each article needs a named author with a bio page listing role, experience and external profiles, plus a link to a public editorial policy that explains how content is researched, reviewed and corrected. Pass: the author page carries Person schema with sameAs links to LinkedIn and other profiles.

14. Primary-source citations, dates and update history

Cite the original study, standard or documentation, not a blog that summarized it. Show the publish date, the last-updated date and a short changelog ("Updated pricing table; replaced screenshots"). Fail: statistics with no source, or a 2023 date on a page that claims to be current.

15. Consistent organization details across the web

Your legal name, category description, headquarters, founders and product names should match across the website, LinkedIn, Crunchbase, G2, Wikidata (where it applies) and press releases. Conflicting facts weaken how a knowledge graph associates your entity. Pass: Organization schema on the homepage lists every official profile in sameAs.

16. Schema chosen per page type and validated

• Article (with author, datePublished, dateModified) on every editorial page.
• Person and Organization for authorship and publisher identity.
• WebPage and BreadcrumbList to place the page within the hub structure.
• FAQPage for question-led sections and HowTo for genuine step procedures; both describe structure even when Google shows no rich result for them.
• Speakable only for short summary passages, and Product only on actual product pages, never on blog posts.

Pass: zero errors in Google's Rich Results Test and the Schema.org validator, and the markup matches the visible content.

17. AI crawlers allowed in robots.txt and at the CDN

Check robots.txt rules for GPTBot, OAI-SearchBot, ChatGPT-User, PerplexityBot, ClaudeBot, Google-Extended and Bingbot. Google-Extended controls whether Gemini models can use your content, while AI Overviews rely on standard Googlebot. Then check your CDN or WAF bot rules, which often block these agents even when robots.txt allows them. This silent failure is easy to miss because nothing on the page looks broken. Pass: server logs show 200 responses to these user agents on priority URLs.

18. Content present in server-rendered HTML and indexed

Many AI fetchers do not execute JavaScript, so copy, tables and links injected client-side can be invisible to them. Confirm with curl or by viewing the raw source. Submit an XML sitemap with accurate lastmod values, and use IndexNow to speed up Bing discovery. Pass: priority URLs are indexed in both Google Search Console and Bing Webmaster Tools.

19. Contextual internal links that reinforce the hub

Place links next to the definitions and claims they support, with descriptive anchors ("agentless scanning", not "click here"). Pass: every spoke sits within three clicks of the homepage, and every priority page has at least three contextual internal links pointing to it.

Distribution and measurement checks (20–25)

Distribution and measurement checks cover the citations you earn outside your own site and the numbers that show whether AI answers are changing.

20. Presence in the third-party sources AI already cites

Go back to the citation map from check 2. If Reddit threads, G2 pages or industry listicles supply the citations for your "best" prompts, you need accurate, helpful presence there. On Reddit, that means disclosed, guideline-compliant replies from real accounts. Fail: bought upvotes, aged accounts or fake reviews, which breach platform rules and damage the brand when they surface.

21. Earned mentions that repeat your entity framing

Digital PR, original data studies (check 10), podcasts and analyst briefings build co-mentions between your brand and the category. Pass: new third-party mentions use your canonical category sentence from check 4.

22. A fixed prompt set tracked weekly

Track the same prompts every week so trend lines stay comparable. The core GEO KPIs:

• Citation frequency: how many prompts cite your domain, per engine.
• Branded mention share: your brand's mentions against competitors' mentions.
• AI Overview inclusion rate across tracked Google queries.
• Passage extraction rate: answers that reuse your wording or tables.
• Indexed supporting pages and entity coverage across the hub.

Generative engine optimization tools differ mainly in whether they stop at measurement or also produce the fix:

On G2 in October 2026, Profound holds 4.6/5 from 1,124 reviews, Scrunch AI 4.6/5 from 73 reviews and Semrush overall 4.5/5 from 3,945 reviews.

Smaller teams can start with free AI tools for SEO optimization and a manual process: Google Search Console and Bing Webmaster Tools for indexing, the Rich Results Test for schema, and the free tiers of ChatGPT, Perplexity and Gemini to run the prompt set by hand in a spreadsheet. As a rule of thumb, that approach holds up to roughly 50 prompts before it turns into a part-time job.

23. AI-origin sessions and assisted conversions

In GA4, create a custom channel group with a source regex such as chatgpt\.com|chat\.openai\.com|perplexity\.ai|gemini\.google\.com|copilot\.microsoft\.com|claude\.ai. Report assisted conversions as well as last-click. Add an "AI assistant" option to the "How did you hear about us?" field, because many AI-influenced buyers arrive later through direct or branded search. Pass: AI-origin pipeline appears as its own line in the monthly report.

24. Accuracy audit of what AI says about you

Once a month, ask each engine about your pricing model, integrations, ideal customer and competitors. Log every factual error and trace it to its source: outdated docs, an old review or a competitor's comparison. Pass: each error has an owner and a corrected source page.

25. A fixed freshness cadence

1. Monthly: re-run the prompt set, check statistics and pricing references, and re-validate schema.
2. Quarterly: replace screenshots, update product names and integrations, and refresh comparison tables.
3. After a major model or AI Overview change: re-sample priority prompts and compare the citation map with the previous one.
4. On every update: change dateModified, add a changelog line and resubmit through IndexNow.

When a generative engine optimization service makes sense

A generative engine optimization service makes sense when you already know the fixes and the bottleneck is producing them across many surfaces. Most generative engine optimization companies sell either tracking or content; Tellr, which publishes this blog, runs both, plus Reddit and paid creative, as one governed program with approvals and an audit trail. Its senior team maps the category's queries, threads and AI answers in week one, agrees the brief and guardrails in week two, then works on a weekly cadence with a monthly program review, as our enterprise GEO guide explains. Tellr is a weaker fit for teams that want a self-serve tool they run themselves.

• Checks 1–2: a category map of prompts, threads and cited sources.
• Checks 6–12: comparison pages, reviews and answer-shaped articles published to WordPress.
• Check 20: guideline-checked Reddit replies behind an approval gate.
• Check 22: weekly citation share, delivered as a digest and queryable through Tellr for Claude, its MCP server.

Fix first: scoring the 25 checks

Fix the checks that block everything else first, then the high-impact, low-effort content fixes, and keep long-cycle authority work running in parallel. A simple score: Priority = Impact (1–5) + Dependency (0–3) − Effort (1–5). Dependency scores 3 when other checks cannot work without this one. The scores below are illustrative for a typical enterprise site; adjust them after your own audit.

Score every failed check the same way, sort by score and re-run the audit each quarter, because model behavior and cited sources shift. Start with access and extractability, measure against a fixed prompt set, and treat this generative engine optimization checklist as a recurring audit rather than a one-off project.

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