Fuel Online Learn · Practical AI SEO + AEO

Turn AI SEO questions into clear next steps.

This free AI SEO course and AEO training help you diagnose missing visibility, publish credible evidence and connect useful AI answers to qualified inquiries. Choose from 30 practical lessons and leave with a concrete work product.

Start with the AI SEO audit Explore the curriculum

30 lessons: One connected curriculum
Worked examples: Follow the reasoning
Implementation labs: Apply the checks

Explore the 30-lesson AI SEO course

Start with the question closest to your business problem. Each lesson includes a clear starting condition and a useful next artifact. Need a route through the material? Use the learning paths below.

Lesson 01

AI SEO Audit: Diagnose Missing ChatGPT Visibility

Use this lesson when your website is absent from sampled AI answers and the cause is unclear. It separates access, retrieval, answer inclusion, suitability and commercial outcomes so you can locate the observable failure. Leave with an evidence ladder and a repair ticket that names the next discriminating test.

Lesson 02

AI Visibility Tracking: Build a Reliable ChatGPT Panel

Use this lesson when visibility scores change but the questions, collection conditions or scoring rules are unstable. Build a fixed buyer-task panel, retain failed observations and distinguish mentions from citations and suitable recommendations. Leave with versioned prompts and explicit denominators that support useful comparisons without representing every buyer.

Lesson 03

AEO Testing: Design Matched-Page AI SEO Experiments

Use this lesson when you want to evaluate a specific page intervention rather than attribute every later improvement to a rewrite. Define pages, matched pairs and observation units, preserve the baseline and inspect spillover. Leave with an assignment and release plan that keeps the comparison interpretable when implementation changes.

Lesson 04

AI Search Citation Optimization: Structure Clear Content

Use this lesson when an extracted answer loses the subject, denominator or limitation that makes a statement accurate. Practice restructuring passages, tables and qualifications around the reader’s question. Leave with an answer that can stand alone while retaining its material conditions, plus an excerpt test for meaning loss.

Lesson 05

AI Search Keyword Research: Map Query Fan-Out to Content

Use this lesson when a large query list is turning into an equally large publishing backlog. Group questions by the buyer decision and evidence needed, then identify existing and justified new owners. Leave with a task-to-URL map that distinguishes observed questions from proposed expansions and avoids redundant destinations.

Lesson 06

AI Crawler Access Audit: ChatGPT Robots and Delivery QA

Use this lesson when intended AI search access may be blocked by robots rules, security controls or delivery challenges. Separate crawler functions and verify request identity before interpreting logs. Leave with a narrow access policy, reproducible checks and a rollback approach rather than an untested instruction to allow everything.

Lesson 07

JavaScript SEO for AI Search: Test What ChatGPT Can Read

Use this lesson when content appears correctly in your browser but decisive facts may depend on JavaScript or interaction. Compare the initial response, rendered page and readable extraction against a critical-fact inventory. Leave with a preservation matrix and regression checks that identify exactly which missing relationships require repair.

Lesson 08

Schema Markup for AI Search: Fix Entity and sameAs Errors

Use this lesson when business names, provider relationships or structured entities contradict visible content. Establish the approved facts before changing the graph, then inspect identity links and delivered markup. Leave with a truth ledger and validation steps that distinguish syntactically valid code from an accurate representation of the business.

Lesson 09

Original Research for AI Citations: Design Evidence Assets

Use this lesson when a buyer question deserves evidence your organization can collect rather than another summary of existing advice. Define the frame, variables, missing states and claim limits before research. Leave with a bounded study design and publication package that others can inspect, reuse and correct responsibly.

Lesson 10

Generative Engine Optimization Testing: Audit GEO Claims

Use this lesson when a GEO headline reports a striking uplift without making its denominator or comparison clear. Reconstruct the original claim, distinguish relative change from percentage points and examine repeated observations. Leave with an experiment critique and decision rules that keep uncertainty attached to the reported effect.

Lesson 11

AI SEO Agency vs In-House: How to Compare AEO Services

Use this lesson when buyers need a fair comparison of service options with different scope, responsibilities or commercial models. Apply eligibility gates before weighted scores, label unknowns and normalize relevant costs. Leave with a defensible procurement comparison whose recommendation can change when the buyer’s requirements or assumptions change.

Lesson 12

How to Get ChatGPT to Recommend Your Business

Start here when you need ChatGPT recommendations to reflect the business’s actual fit, supported capabilities and current evidence. Apply mandatory eligibility gates before preferences, inspect source support and test appropriate clarification or exclusion. Leave with a recommendation audit and an agency brief that separates verified repairs from later answer observations.

Lesson 13

Local SEO for ChatGPT: Verify Business Recommendations

Use this lesson when a local recommendation names a real business but gets the service, operating hours or geographic fit wrong. Reconcile branch facts with actual operations and test place, time and customer need separately. Leave with a suitability rubric and a correction plan for the responsible source.

Lesson 14

AI SEO for Ecommerce: Keep Prices and Stock Accurate

Use this lesson when product prices or availability disagree across a page, feed, markup and checkout. Follow a variant-level promotion and stock timeline, distinguish source changes from observed answers and prioritize consequential discrepancies. Leave with a reconciliation process that identifies where the current offer stopped propagating correctly.

Lesson 15

AI SEO for SaaS: Optimize Integration Pages for AI Search

Use this lesson when an integration page promises a connection without explaining its actual supported workflow. Specify objects, direction, permissions, plan limits, retries and responsibility across native, connector and custom paths. Leave with a capability contract that helps a buyer distinguish supported delivery from a merely possible implementation.

Lesson 16

Reddit and X for AEO Research: Verify AI SEO Claims

Use this lesson when Reddit or X discussions suggest a promising AEO tactic but the evidence is fragmented or promotional. Audit original claims, duplicate reports, baselines and counterexamples before designing a bounded test. Leave with a research worksheet and participation principles that protect community usefulness and honest attribution.

Lesson 17

Digital PR for AI Search: Build Evidence Worth Citing

Use this lesson when you have a useful finding to distribute and need its meaning to survive headlines, summaries and coverage. Check selection effects, build a verifiable source package and separate distribution from later citations. Leave with claim boundaries and correction controls that keep earned attention connected to evidence.

Lesson 18

Content Freshness for AI Search: Maintain Accurate Facts

Use this lesson when changing terms, capabilities or policies affect several pages and resources. Classify volatility and buyer consequence, identify dependencies and separate publication from effective dates. Leave with an approved change manifest, review triggers and rollback conditions that keep current statements from contradicting historical customer agreements.

Lesson 19

AI Search Referral Tracking: Connect ChatGPT Visits to CRM

Use this lesson when AI referral reports cannot be reconciled with contacts, opportunities or revenue. Preserve observed acquisition evidence and self-report separately, define qualifying events and count unique opportunity cohorts. Leave with CRM fields and a reconciliation method that avoids treating duplicate contacts or unknown origins as additional sales.

Lesson 20

90-Day AEO Implementation Plan: Scope, Budget and Review

Use this lesson when an AEO program needs realistic sequencing and resource decisions rather than a long unordered checklist. Map dependencies, assign accountable owners and budget a fictional capacity example with explicit reserve. Leave with a staged plan whose acceptance, repair and stop rules make scope tradeoffs visible.

Lesson 21

Outdated Business Information in ChatGPT: How to Correct It

Use this lesson when ChatGPT repeats an old contract, price or business policy after you believe it was corrected. Trace the exact statement through cited sources, public delivery and repeated observations. Leave with a source-specific correction record that distinguishes stale owned content, external copies and an unsupported answer.

Lesson 22

AI Citation Analysis: Why ChatGPT Cites Competitors

Use this lesson when a competitor recommendation includes citations but you do not know which sources support its decisive claims. Inspect exact passages, publisher roles and current conditions, then distinguish evidence gaps from distribution problems. Leave with a source-to-claim matrix and a correction or research action tied to buyer fit.

Lesson 23

How to Test Multilingual AI Search Visibility by Country

Use this lesson when visibility or accuracy differs across languages and countries. Separate language, market and actual offer conditions, compare native-intent questions with equivalent controls and preserve local scoring references. Leave with a paired-market matrix that identifies consequential errors without treating translation differences as an automatic causal explanation.

Lesson 24

How to Find B2B AI Search Keywords in Sales Calls

Use this lesson when your strongest buyer questions are hidden in sales conversations rather than a keyword list. Extract authorized wording, distinguish interpretation from evidence and code the decision-changing constraint. Leave with question-and-proof briefs that connect recurring objections to appropriate content owners without inventing conversational search volume.

Lesson 25

Author Expertise for AI Search: Build Credible E-E-A-T

Use this lesson when a byline or expert-review label says little about who verified the article’s claims. Match technical, analytical and commercial assertions to actual relevant contributions and preserve their approved scope. Leave with a responsibility matrix and truthful attribution that can be maintained when the content changes.

Lesson 26

PDF vs HTML for AI Search: Which Should You Publish?

Use this lesson when research needs a readable webpage, a downloadable report or reusable data and their versions may diverge. Test extraction, preserve table relationships and distinguish equivalent editions from related resources. Leave with a format decision and version map that keeps corrections understandable across the publication package.

Lesson 27

AI SEO Case Study: Measure Results Without False Attribution

Use this lesson when you want to publish an AI SEO case study without overstating what the evidence establishes. Preserve the baseline, shipped changes, comparison and distinct business outcomes before writing the headline. Leave with a claim ladder and evidence package that separates completed work from measured results.

Lesson 28

How to Audit AI SEO Content Quality Before Publishing at Scale

Use this lesson when a content program must accept or repair many pages without confusing template completion with usefulness. Review distinct intent, claim support, substantive reasoning, real visuals and delivered experience. Leave with page-specific release states and dependency-based holds that let accepted independent work continue while defects are repaired.

Lesson 29

ChatGPT Brand Confusion: Diagnose Business Identity Errors

Use this lesson when ChatGPT merges your business with a namesake, former brand, parent or unrelated product. Establish approved identity relationships and inspect the source behind each mistaken assertion. Leave with ambiguous, contextual and exact-identity question groups that distinguish genuine resolution from a prompt that supplies the answer.

Lesson 30

AI Search Conversion Optimization: Turn Visits Into Leads

Use this lesson when AI search visitors reach the right page but cannot verify fit or complete a useful next action. Continue the cited task through evidence, qualification, forms and operational routing. Leave with a tested landing path and separate measures for submissions, qualified inquiries and unresolved source information.

How to use this course

Use the AI SEO training to solve a specific discovery, accuracy or buyer-experience problem. Begin with a question your business needs to answer: can the right page be accessed, does the answer preserve its conditions, is the recommendation suitable, or can an interested visitor take the next step? Choose the lesson that addresses that decision, then leave with a concrete test, evidence record or implementation artifact.

You can begin this AEO course at the lesson matching your responsibility. A developer investigating missing JavaScript content needs a different starting point from a marketing team reconciling conflicting prices. A researcher assessing a citation claim needs different evidence from a sales team investigating an unassigned inquiry. Use the paths below to choose the work that changes your next decision, then follow a related lesson only when its method resolves a real dependency.

Keep five outcomes separate while you work: a page being available, a fact being represented accurately, a source being cited, an offer being appropriately recommended and a qualified commercial inquiry being recorded. One observation does not establish the next outcome. A correctly delivered page can still be absent from a sampled answer; a cited page can still be summarized incorrectly. Recording the exact outcome makes the next repair clearer than an unexplained visibility score.

Apply this answer engine optimization course with a small working packet for the question you need to resolve. Include the buyer question, affected destination, current approved facts, exact observed answer when available and the decision you need to make. Add the date and relevant market, language or product conditions. Where information is missing, preserve that uncertainty. This packet lets you apply the exercises to an actual problem without treating a teaching example, a selected screenshot or a colleague’s recollection as measured evidence.

The lessons use worked examples to expose assumptions, calculations and decision rules. Treat illustrative inputs as practice material, then replace them with verified facts before using the method commercially. Retain useful limitations when sharing a conclusion. A pricing calculation needs its commitment and usage assumptions; a comparison needs its eligibility rules; a lead report needs its qualification definition. The method is complete when another responsible person can understand what was checked and what remains unresolved.

Five implementation paths

Find the failing stage before commissioning more content

Start with the AI search visibility diagnosis when the symptom is broad or uncertain. Use the prompt-panel method to preserve a repeatable observation process. Route an access problem to the crawler lesson, a missing-fact problem to content extraction, and an incorrect business statement to source diagnosis. Finish this path with one bounded repair ticket: the disputed outcome, supporting observation, responsible owner and verification condition. Avoid assigning several unrelated changes to one unexplained score.

Repair commercial facts before increasing exposure

Choose the lesson matching the fact that could change a buyer’s decision: recommendation eligibility, local service boundaries, product stock or integration support. Build the relevant fact contract and inspect the public surfaces that repeat it. Then use content freshness and change control to coordinate dependent updates. This path produces an approved fact record and a checked release, followed by a separate observation of later AI answers. It does not turn a successful website correction into proof that every external copy changed.

Build useful evidence and make it reusable

Use original research design when a consequential question lacks a credible answer your organization can investigate. Establish the sample, variables and limits before collection. Continue to PDF versus HTML publishing when readers need maintained files, and to digital PR evidence distribution when findings will circulate beyond the source page. The deliverable is a traceable finding with usable supporting material, rather than a statistic created merely to decorate a campaign.

Evaluate an optimization claim before scaling it

Use the GEO claims lesson to reconstruct the outcome, denominator and comparison behind an uplift headline. Move to matched-page AEO testing when you have an intervention and suitable units to assign. Use case-study measurement before publishing the result. This path keeps a descriptive observation, a comparative estimate and a causal interpretation distinct. Its practical output is a decision record that states whether to repair, continue observing, redesign or expand the work.

Connect a useful answer to a qualified inquiry

Start with the AI search visitor landing-path lesson when visitors reach a relevant page but cannot find proof, choose an appropriate action or complete the handoff. Check fit criteria, confirmation and operational routing. Then use CRM referral attribution to preserve the source evidence you can actually observe and reconcile unique opportunities. The output is a verified reader journey and an honestly defined report, rather than a larger form-submission count whose commercial meaning remains unknown.

Diagnose the problem before choosing a lesson

The page cannot be reached or its decisive facts disappearInspect access and delivered meaning before rewriting the explanation. Crawler access distinguishes intended controls and observed requests; JavaScript content testing compares initial, rendered and extracted representations. Record the exact destination and missing fact. A successful status or attractive screenshot is insufficient when a buyer-changing exclusion, price or eligibility condition does not survive the tested delivery path.
The answer is stale or combines unrelated business identitiesChoose outdated-information diagnosis for a previously valid fact that no longer applies. Choose brand-confusion diagnosis when the answer merges names, organizations or relationships. Use the schema lesson for implementation after the real identity is established. These problems can coexist, but they need separate factual references and acceptance tests; changing a date cannot repair a false identity relationship.
The content exists but the question still lacks a useful answerUse query-to-content mapping to identify the reader decision and its proper owner. Use sales-call question research when you need better evidence of buyer wording and constraints. A new phrase does not automatically justify a new page. Identify the missing answer, proof or functional artifact, then decide whether an existing destination can serve it.
A recommendation appears, but its suitability is uncertainInspect the decisive claim and its support. Use the business recommendation audit to establish mandatory fit before counting brand exposure. Competitor source analysis helps distinguish a missing fact from an unsupported recommendation or distribution gap. Use the recommendation audit, local, comparison or integration lesson when those constraints determine fit. Success means an accurate answer for the buyer’s conditions, including an appropriate exclusion when the offer cannot meet them.
A chart shows improvement, but the business conclusion is unclearRebuild the numerator, denominator, comparison and observation conditions. Keep question-panel outcomes separate from website sessions and qualified inquiries. The experiment, case-study and attribution lessons address different parts of this chain. Name which event was observed and which relationship remains unverified. A precise percentage should clarify the evidence rather than hide a changed sample, duplicated opportunity or unfinished follow-up period.
The team needs to release many changes without losing qualityUse the 90-day implementation plan to allocate capacity, dependencies and reserve. Apply content quality release gates to the actual artifact, and authorship evidence when a claim needs scoped expert review. Keep an unresolved defect attached to the affected item. A polished template or completed checklist does not replace a distinct answer, factual support and verified delivery.

Finish with a reusable work record

After applying a lesson, record the question, approved reference facts, action taken, observed result and remaining uncertainty. Keep the artifact small enough for the next responsible person to use. If a test fails, identify the specific repair and the condition for retrying. If the evidence supports only a descriptive finding, preserve that limit. Return to another lesson when it resolves the next dependency, rather than expanding the project simply because more lessons are available.

The Fuel Online Learn hub brings together practical guides to AI SEO, Answer Engine Optimization (AEO), Generative Engine Optimization (GEO) and the website work that supports useful search experiences. Start with the problem you need to solve: making important information accessible, explaining an offer accurately, evaluating a citation or helping a qualified visitor take the next step. The right method depends on the question, the platform and the evidence available.

Use the guides to turn a broad visibility concern into a specific task and a record you can verify. Keep website changes, observed AI answers and commercial outcomes separate. Clear content can improve the information you publish, but no formatting choice or schema setting guarantees inclusion, citations or a particular recommendation.

Four areas of AI search and SEO practice

1. Generative Engine Optimization (GEO)

GEO focuses on whether useful content appears as supporting evidence in generated answers and whether those answers represent it accurately. It overlaps with SEO work such as clear explanations, accessible pages, relevant evidence and coherent navigation. Begin with the buyer’s question, provide the facts and qualifications needed to answer it, and inspect any observed citations for actual support. A brand mention, a source citation and a suitable recommendation are different outcomes; none should be substituted for the others.

2. Answer Engine Optimization (AEO)

AEO emphasizes accurate, understandable answers to specific questions. Put the answer near the beginning, name its subject and preserve conditions such as price scope, compatibility or eligibility. Use headings, steps and tables when they help the reader complete the task. Relevant structured data should describe the visible content truthfully; it does not replace a useful explanation. Test what survives delivery and extraction rather than assuming a fixed passage length or special markup makes an answer selected.

3. Entity clarity and trustworthy information

Entity clarity means identifying the right organization, person, product or service and explaining their relationships accurately. Start with consistent visible names, current descriptions and verified facts. Distinguish a product from its provider, a subsidiary from its parent and a business from an unrelated namesake. Experience, expertise, authoritativeness and trustworthiness are useful considerations when assessing content, but they do not create immunity from search changes. Publish only real credentials, contributions and results, with appropriate scope and responsibility.

4. Qualified inquiries and the customer journey

Useful visibility should connect to an appropriate next action. For a roofing or construction business, that can mean explaining service areas, project requirements and what happens after an inquiry. Make the contact path usable, route requests to the right owner and choose follow-up commitments the team can actually meet. Verify the handoff rather than assume a success message proves delivery. Measure qualified inquiries, opportunities and completed sales separately; a fast response or larger submission count alone does not establish a higher closing rate.

Featured platform guides

Explore these platform guides alongside the practical lessons above. Check current provider documentation before applying a technical change, and evaluate observed answers without assuming a universal set of required citation signals.

ChatGPT visibility and AI search optimization Explore how to make relevant information accessible and assess whether observed answers use it accurately.
Claude, AEO and AI search optimization Consider the buyer question, available supporting sources and the limits of what one answer can establish.
Perplexity and AI search visibility Investigate discovery, current information and source support while keeping a website update separate from later answer observations.
Gemini and AI search optimization Examine content clarity and verification methods without treating schema or formatting as a guaranteed selection mechanism.

Common questions about SEO and AI search

What is the difference between SEO and GEO? SEO addresses a website’s discoverability, relevance and usefulness in search. GEO focuses on its representation in generated answers, including source use and factual accuracy. The work overlaps: a useful answer still needs credible facts, readable structure and an accessible destination. Measure rankings, visits, mentions, citations and recommendations according to their own definitions rather than treating them as one result.

How should construction and roofing businesses evaluate lead generation? Define a qualified inquiry for the actual service, geography and project requirements. Check the page, form, confirmation and routing process, then reconcile unique inquiries with later opportunities and sales. Record the source evidence you can observe and retain unknown origins. Assess response times against real records and approved commitments; do not assume a particular response speed guarantees revenue or exclusive leads.

How can clearer entity information help? Specific names, categories and relationships help readers distinguish the right business and understand its offer. Correct contradictory descriptions and verify identity references before adding structured data. Keep important facts in the visible explanation. This creates more coherent information for people and search systems, while later observations are still needed to determine how a particular engine represents it.

AI SEO Course and AEO Training: 30 Practical Lessons