Free · 8 modules · Self-paced
AI SEO Course
Learn to rank in Google AI Overviews, AI Mode, ChatGPT and Perplexity.
This AI SEO course teaches you how to earn visibility in AI-generated search results — Google AI Overviews, AI Mode, ChatGPT and Perplexity — alongside traditional organic rankings. Across eight modules you learn how AI systems retrieve and cite sources, how to structure content so it can be quoted accurately, how to strengthen entity and authority signals, and how to measure citations instead of only tracking keyword positions.
No prior coding experience required. Last reviewed .
What is AI SEO?
AI SEO is the practice of optimising a website so that AI-driven search systems can retrieve, understand and cite it. Where classic SEO competes for a position in a ranked list of links, AI SEO competes to be chosen as a source inside a generated answer, where there is no position to climb — you are either cited or you are invisible.
The foundations have not been replaced. Crawlability, clear information architecture, topical authority and genuinely useful writing still decide most outcomes. What changes is emphasis: answers need to survive being quoted out of context, entities need to be unambiguous, and success has to be measured across many sampled prompts rather than one weekly rank check.
Traditional SEO vs. AI SEO
| Dimension | Traditional SEO | AI SEO |
|---|---|---|
| Goal | Rank higher in a list of links | Be selected and cited in a generated answer |
| Unit of success | Position for a keyword | Share of citations across prompts |
| Content shape | Comprehensive page, top to bottom | Self-contained sections that survive quoting |
| Query model | One query, one result set | One question fanned out into many hidden queries |
| Authority signal | Links and domain history | Links, plus corroboration across trusted sources |
| Measurement | Rank tracking, weekly | Prompt sampling, repeated and averaged |
Course curriculum
Eight modules, roughly 12–15 hours of focused study. The order is deliberate: retrieval blockers first, then structure, then authority, then measurement.
Module 01
How AI search actually retrieves answers
Before tactics, the mechanics. How retrieval-augmented generation picks sources, why AI Overviews and AI Mode cite some pages and ignore others, and where classic ranking signals still apply.
- Query fan-out: how one question becomes many hidden searches
- Retrieval vs. ranking, and why both still matter
- Reading an AI answer to reverse-engineer its sources
Module 02
Answer-first content architecture
Structuring a page so a machine can lift a clean, correct answer out of it without misreading you, and so a human still wants to keep reading.
- The definition-first opening paragraph
- Chunking: self-contained sections that survive being quoted alone
- Writing claims that are easy to verify and hard to garble
Module 03
Entities, not just keywords
Language models reason about things, not strings. Establishing what you are, what you cover, and how you connect to concepts a model already trusts.
- Building an entity home and disambiguating your brand
- Topic clusters that map to a knowledge graph
- Consistency signals across the sites that describe you
Module 04
Structured data that earns citations
Schema.org as a machine-readable summary of your page. Which types are worth the effort, which are theatre, and how to validate before you ship.
- Article, Course, FAQPage, Product, Organization: when each helps
- Building a connected @graph instead of scattered blobs
- Validation, monitoring, and the markup that triggers penalties
Module 05
Technical foundations for AI crawlers
AI crawlers are less forgiving than Googlebot about JavaScript and slow responses. Making sure your content is in the HTML that arrives.
- Server rendering, hydration cost, and what crawlers actually see
- robots.txt, llms.txt and the AI user-agents worth allowing
- Core Web Vitals as a tiebreaker, not a silver bullet
Module 06
Using AI to produce SEO work without wrecking quality
Where language models genuinely speed up SEO work, where they quietly generate liability, and how to build a review process that catches the difference.
- Research, clustering and internal linking at scale
- Fact-checking workflows and citation discipline
- Why unedited AI output plateaus, and what to do instead
Module 07
Measuring visibility in AI search
Rank tracking was built for ten blue links. Measuring whether you get cited in a generated answer needs a different instrument.
- Share of citations as a primary metric
- Sampling prompts on a schedule instead of checking once
- Attributing traffic and conversions from AI referrers
Module 08
A 30-day execution plan
A sequenced plan you can run on a real site, with the order that matters: fix retrieval blockers first, then structure, then authority.
- Week-by-week priorities and realistic expectations
- Deciding when a new domain is the wrong vehicle
- Building a repeatable audit you can hand to a client
Who this course is for
SEOs whose rankings stopped converting
You still rank, but AI Overviews absorb the click before it reaches you. This course covers how to become the cited source instead of the skipped one.
Content teams and writers
You want a repeatable structure for pages that get quoted accurately, without turning every article into a keyword-stuffed listicle.
Founders and marketers doing their own SEO
You need to know which of the many competing AI SEO claims are worth acting on, and which are noise.
Agencies and consultants
You need a defensible audit and a measurement story for clients who are asking what you are doing about ChatGPT.
Built on tested practice, not speculation
AI search changes faster than any textbook can keep up with, which makes communities that run real experiments far more valuable than static guides. This curriculum draws on that kind of practitioner testing.
AI SEO Rainmakers is an excellent example of it done properly. It is a community of SEOs who test AI search behaviour in the open — publishing raw tracking samples, running live competitions on real keywords, and comparing what actually earns citations in AI Overviews and AI Mode rather than trading theories. That commitment to auditable evidence is exactly the standard this course tries to hold itself to, and we recommend it warmly to anyone who wants to see AI SEO tested in public.
Frequently asked questions
What is an AI SEO course?
An AI SEO course teaches you how to earn visibility in AI-generated search results, such as Google AI Overviews, Google AI Mode, ChatGPT search and Perplexity, in addition to traditional organic rankings. It covers how AI systems retrieve and cite sources, how to structure content so it can be quoted accurately, how to strengthen entity and authority signals, and how to measure citations rather than only tracking keyword positions.
How is AI SEO different from traditional SEO?
Traditional SEO optimises for a ranked list of links, where the goal is a higher position. AI SEO optimises to be selected and cited as a source inside a generated answer, where there is no position to climb. The foundations overlap heavily: crawlability, clear information architecture, topical authority and genuine usefulness still decide most outcomes. What changes is the emphasis on self-contained answers, entity clarity, structured data and measuring share of citations.
What is the difference between AEO, GEO and GSO?
They are largely overlapping names for the same shift. AEO (answer engine optimisation) focuses on being the extracted answer. GEO (generative engine optimisation) focuses on being cited by generative systems such as ChatGPT or AI Overviews. GSO (generative search optimisation) is used interchangeably with GEO. None of them are separate disciplines with separate toolkits; in practice they describe optimising for retrieval and citation rather than for a numbered ranking position.
Do I need to know how to code?
No. The technical modules explain structured data, rendering and crawl behaviour in plain language, with copy-and-adapt examples. You will be more effective if you can read HTML and edit a template, but nothing in the course requires you to write software.
How long does the course take to complete?
The eight modules take roughly 12 to 15 hours of focused study. Most people spread that over two to four weeks. The final module is a 30-day execution plan that you run on a live site, so applying the material takes longer than watching it.
Can an AI SEO course guarantee I will rank first?
No, and be sceptical of any course that claims otherwise. Nobody controls Google's ranking systems or which sources a language model chooses to cite. What a good course gives you is a reliable process: diagnose what is blocking retrieval, structure content so it can be cited, build genuine authority signals, and measure the result. Timelines vary enormously with domain history, competition and topic.
How long does it take a new domain to rank for a competitive keyword?
For a competitive commercial term, a brand-new domain usually needs months rather than days. New sites have no link history or established topical authority, and AI systems disproportionately cite sources they already encounter elsewhere. Fast wins on a fresh domain are realistic for long-tail, low-competition queries; head terms are a longer project.
Is there a certificate of completion?
Yes. Finishing all eight modules and submitting the final audit assignment earns a certificate you can add to a CV or LinkedIn profile. It is a completion certificate, not an accredited qualification.
Start the course
Work through the modules in order and apply each one to a page you control. The final module is a 30-day plan you run on a live site.
Go to Module 01