Curation in progress
SEO
Ten modules on the layer that decides whether anyone ever arrives. It starts where no other SEO course does: what a crawler actually fetches, why your page is stored as a posting list rather than as a page, the arithmetic BM25 does to decide one document beats another, and PageRank as an eigenvector over a link graph. Then how an AI answer is really assembled - embeddings that match a page sharing none of your words, retrieval and grounding, one question fanning out into a dozen searches, and how a passage becomes a citation. Only then the practice: robots.txt and the AI crawlers, rendering and canonicals, Core Web Vitals and the page an agent sees, intent and topic architecture, content with evidence behind it, links and mentions and entities, and measuring any of it honestly across two consoles when the click may never happen. It ends with the part SEO courses skip - what a visit is worth, and the loop that turns one user into another.
- Modules
- 10
- Lessons
- 52
- Watch time
- 46h 48m
- Free to open
- 4
How Search Actually Works
Understand the machine before the tactics: what a query is, how pages are fetched, how they are stored, and what 'relevant' is computed from.
- 01.01What a Query Actually IsBreak a single search phrase into the intent, the implied constraints and the ambiguity a ranking system has to resolve, and explain why 'best crm' is three different questions wearing one set of words.
- 01.02The Crawler: Frontier, Budget and PolitenessDescribe how a crawler chooses what to fetch next, why crawl budget exists at all, and what Googlebot actually requests when it visits a page.
- 01.03The Index: Your Page Is Not Stored as a PageExplain tokenisation and the inverted index, and say what actually gets looked up when someone searches - a posting list, not your document.
- 01.04Scoring Text: TF-IDF and BM25Compute why one document scores higher than another for the same term, and explain what BM25's saturation and length-normalisation terms are correcting for.
- 01.05Links as Votes: PageRank and What It BecameExplain PageRank as an eigenvector over a link graph, and say honestly which parts of that idea still operate and which were replaced.
How AI Answers Are Built
An AI answer is a retrieval result with a writer on top. Learn every stage of that pipeline, so a change to it is something you can reason about rather than react to.
- 02.01Embeddings: Matching a Page That Shares No WordsExplain how text becomes a vector, why cosine similarity retrieves a relevant page containing none of the query's words, and what that model is blind to.
- 02.02Grounding: An AI Answer Is a Search Result With a Writer on TopTrace a generative answer back through grounding to the retrieved documents it was built from, and say which stage a missing citation actually failed at.
- 02.03Query Fan-Out: One Question Becomes Twelve SearchesDescribe how a single prompt is expanded into a set of concurrent related queries, and explain why that makes topic coverage matter more than any single keyword.
- 02.04Rerankers: How a Passage Becomes a CitationExplain what a reranker does that first-stage retrieval cannot, and describe the path from candidate document to the specific passage a model cites.
- 02.05The Answer Engines, and Whose Index Each One UsesName what actually backs AI Overviews, AI Mode, ChatGPT Search, Copilot and Perplexity, and explain why being absent from one index removes you from more than one product.
- 02.06Why the Same Prompt Answers Differently TwiceExplain the sources of non-determinism in a generated answer - sampling, personalisation, freshness, fan-out variance - and say what that does to any measurement built on prompts.
Being Retrievable At All
Everything in module 1 and 2 is unreachable if the page is never fetched, never indexed, or never rendered. This is the floor.
- 03.01robots.txt, Status Codes, and Blocked vs NoindexChoose correctly between disallow, noindex, a 404, a 410 and a canonical for a given page, and explain why a blocked page can still rank while a noindexed one cannot.
- 03.02The AI Crawlers: GPTBot, OAI-SearchBot, Google-ExtendedWrite a robots.txt that allows the crawlers which make you citable while refusing the ones that only take training data, and explain what each decision costs.
- 03.03Rendering: How an SPA Goes InvisibleDiagnose whether content exists in the HTML, after rendering, or nowhere a crawler will see it, and pick between SSR, prerendering and hydration for a given page.
- 03.04Canonicals, Duplicates, and the URL as a Permanent KeyResolve a duplicate-content situation with the right combination of canonical, redirect and parameter handling, and explain why a URL is a contract you cannot casually break.
- 03.05Site Architecture: The Graph You Actually ControlDesign an internal link structure that puts your most important pages within reach of a crawler and a reader, and explain why this is the only part of the link graph you own.
- 03.06Sitemaps, IndexNow, and the Path Into BingPublish a change and get it into an index the same day, and explain why Bing's index being the backing store for ChatGPT Search and Copilot makes Bing Webmaster Tools an AI-search surface.
Structure a Machine Can Read
What has to be true about the page itself - for a parser, for a performance measurement, and now for an agent operating it on someone's behalf.
- 04.01Semantic HTML: Writing for a Parser Without Writing for OneMark up a page so its structure is legible to a machine without distorting it for a reader, and explain why Google says chunking content for AI is unnecessary.
- 04.02Structured Data: What It Earns and Where It Is Not RequiredAdd the schema types that actually produce a result feature for a given page, and say plainly which claims about structured data and AI visibility are unsupported.
- 04.03Core Web Vitals: LCP, INP and CLSSay what each of the three metrics measures, at which percentile and over what window, and diagnose which one a slow page is actually failing.
- 04.04Field vs Lab: CrUX, RUM and LighthouseExplain why a Lighthouse score and a CrUX report disagree, and choose which one to trust for a given decision.
- 04.05The Page an Agent Sees: DOM, Screenshot, Accessibility TreeDescribe the three ways a browser agent perceives a page, and change a component so an agent can operate it - which is the accessibility audit restated.
Intent and Topic Architecture
Deciding what to write, when the question arrives as a conversation and gets expanded into a dozen searches before anything is retrieved.
- 05.01Keyword Research When the Query Is a ConversationBuild a keyword set from demand data and turn it into a set of questions a fan-out would generate, rather than a list of phrases to repeat.
- 05.02Reading the SERP as the Answer Key for IntentInfer what a ranking system believes a query means by reading what it already returns, and decide whether your page format can compete at all.
- 05.03Topic Clusters and the Entity Behind the WordsDesign a cluster of pages around a topic and its entity rather than around keyword variants, and explain what an entity is to a search system.
- 05.04The Page That Answers FirstRestructure a page so its answer is available in the first screen and to a retrieval system, without turning it into a list of disconnected fragments.
- 05.05Decay, Refresh and ConsolidationIdentify pages that are losing ground, decide between updating, merging and removing each one, and explain why freshness is weighted the way it is in AI citations.
Content With Something Behind It
The difference between content that is technically fine and content a system has any reason to prefer - and the line where scaling it becomes a policy violation.
- 06.01What 'Unique, Useful, People-First' Means to a Ranking SystemEvaluate a page against the helpful-content criteria the way a quality rater would, and name what a ranking system can actually detect about it.
- 06.02Original Research and Proprietary DataTurn something only you can measure into a page nobody can copy, and explain why this is the one content type that reliably earns both links and citations.
- 06.03E-E-A-T as Evidence, Not a CheckboxProduce the concrete artefacts that demonstrate experience and expertise on a page, and say what E-E-A-T is and is not inside Google's systems.
- 06.04Programmatic SEO: The Dataset That Earns the PageDesign a template-plus-dataset system where each generated page answers a distinct question, and state the test that separates it from mass-produced filler.
- 06.05Scaled Content Abuse: Where the Line IsState Google's actual position on AI-generated content, identify which side of the scaled-content-abuse line a given plan falls on, and describe what enforcement looks like.
Authority, Mentions and Distribution
Where credibility comes from now that a citation can replace a click, and why the off-site graph stopped being a side quest.
- 07.01Links: What PageRank Still BuysJudge whether a link is worth pursuing, and describe honestly what the evidence says about link influence relative to other signals in 2026.
- 07.02Digital PR and the Earned MentionRun a campaign whose output is coverage rather than a link exchange, and explain why an unlinked mention now carries weight it did not before.
- 07.03The Off-Site Graph: Reddit, YouTube and WikipediaName the third-party surfaces that answer engines cite most, decide which ones your product should legitimately be present on, and read the conflicting citation studies critically.
- 07.04The Entity: Being One Thing EverywhereMake a brand resolvable as a single entity across the sources a model reads, and diagnose the collisions and inconsistencies that stop it.
- 07.05Brand Demand: The Asset That Survives the ClickExplain why brand search volume behaves as an owned asset in an answer-first world, and set up the loop that grows it.
Measurement
Knowing whether any of it worked - across two consoles, a set of vendor tools that measure different things, and a channel where the click may never happen.
- 08.01Search Console, HonestlyRead impressions, clicks, CTR and average position for what they actually are, and avoid the four aggregation traps that make the report say something it does not.
- 08.02The Generative AI Reports: What They Contain and What They Do NotUse the Search Console generative-AI reporting for the questions it can answer, and state precisely which dimensions are absent and when the data begins.
- 08.03Bing Webmaster Tools and the AI Performance ReportInstrument the Bing side of AI visibility, and explain why a team that ignores it is blind to the index behind ChatGPT Search and Copilot.
- 08.04AI Visibility Tools: What a Score Is WorthCompare what the AI-visibility platforms actually sample and measure, and explain why two credible 2026 studies can put Reddit at forty percent of citations and under five.
- 08.05Attribution Without a Click, and Experiments That Can Be WrongDesign an SEO experiment with a control and a stated failure condition, and build an attribution picture from branded search, direct, referral and self-reported data.
Turning a Visit Into a User
The seam nobody owns: the ten seconds between arriving from a search and deciding to stay.
- 09.01The Landing Seam: Matching the Page to the Intent That ArrivedDiagnose a mismatch between the query that produced a visit and the page it landed on, and fix it without building another page.
- 09.02The Answer-First Landing PageWrite and structure a landing page that states what the product is and who it is for above the fold, and explain why this serves the reader and the retrieval system at once.
- 09.03Time-to-Value: What Someone Must Reach Before They StayDefine the first moment of real value in a product, measure how long it takes to reach, and identify what stands between arrival and that moment.
- 09.04Pricing Pages, Free Tiers and the Trial That ConvertsChoose between free trial, freemium and a usage cap for a given product, and build a pricing page that answers the question a visitor actually arrived with.
- 09.05Instrumenting the Funnel: Impression to Activated UserWire one event schema that follows a person from search impression through to activation, and explain why a funnel assembled from four disconnected tools tells you nothing.
The Growth Loop
What happens after the first session, and the cadence that makes any of this compound instead of being a campaign.
- 10.01Activation, and Why It Beats Acquisition VolumeDefine an activation event from behaviour that predicts retention rather than from a feature tour, and measure it.
- 10.02Retention and the Shape of a Good Cohort CurveRead a cohort retention curve, tell a flattening curve from a decaying one, and say what each shape implies about whether growth is worth spending on.
- 10.03Referral and Viral LoopsDistinguish a genuine loop from a referral programme, identify whether a product has one available to it, and calculate what it actually returns.
- 10.04Product-Led Sales and ExpansionIdentify the usage signals that make an account worth a conversation, and design the hybrid self-serve-plus-sales motion that follows.
- 10.05The Discovery Operating SystemAssemble everything in this course into a weekly and quarterly cadence a small team can actually run, and decide what to automate and what must never be.