9 stages and 91 topics, taking generative engine optimization from the retrieval pipeline to the monthly report. Every number is attributed to a named study with its sample size and date, and every source link goes to a research paper or an engine's own documentation — never to a tool vendor selling you the conclusion.
Generative engine optimization (GEO) is the practice of making a page likely to be retrieved, quoted and attributed inside an AI-generated answer — in ChatGPT, Perplexity, Google AI Overviews and AI Mode, Gemini, Claude or Copilot. It is not a separate channel from SEO: the retrieval layer underneath every one of these systems is a search index, and the strongest finding in the academic literature is still that a document's relevance and its position in the retrieved context dominate everything you can do to its wording. What GEO adds is a different unit of work (a passage, not a page), a different outcome (a mention, not a click) and a measurement problem that classical SEO never had: ask the same question twice and you get two different answers.
Start from what you already know. The stages are ordered, but three of them are entry points rather than prerequisites.
Read stage 1 in order. You need the retrieval pipeline in your head before any tactic makes sense, because most bad GEO advice is a tactic applied at the wrong stage of it.
Skip to “what genuinely changes” in stage 1, then stage 3. Your ranking work is not wasted — it is the input. What changes is the unit you optimise and the fact that a rewrite which helps quotability can hurt retrievability.
Go straight to stage 2. Most dashboards in this category report a point estimate from a single run, which is the one thing the statistics say you must not do.
9 stages, 91 topics. Each stage collapses, and every topic links straight to the section that answers it rather than to the top of an article.
Start here. What an AI answer engine actually is, the five stages between a question and a citation, and which of your search instincts still apply. Almost every piece of bad GEO advice is a tactic aimed at the wrong stage of that pipeline, so this stage is what lets you sort the rest.
Prompts carry no search volume and one run of one prompt is not a measurement. This stage is the method: build a panel from demand you can observe, then sample it often enough that the number survives a statistician looking at it.
Engines quote passages, not pages: in the largest published passage dataset the median quoted passage ran 117 words and about 85% stood alone. Four content levers have real evidence, two test null or negative, and one popular rewrite costs more at retrieval than it gains at synthesis.
Retrievability is the ceiling on everything else: one audit found 27.1% of URLs cited in AI answers could not even be fetched. This stage is mostly first-party vendor documentation, and the most expensive mistake in it is blocking the wrong crawler token.
Roughly nine tenths of a commercial answer is assembled from pages you do not own, and no amount of writing on your own domain changes that ratio. Entity consistency, third-party coverage, and how community threads get selected, which is by format and semantic match rather than by popularity.
A 90-day plan, four numbers worth reporting, and the honest version of the ROI conversation. Most GEO programs are cancelled in month four because somebody promised a revenue number the evidence never supported, so the reporting group here matters more than the plan.
The failure modes that end GEO programs, and the widely repeated claims this curriculum refuses to teach. Every entry here has a mechanism for failing that does not require anyone to care about ethics, which is why they are worth knowing before you start rather than after.
Nothing in this curriculum requires a paid plan. The first two produce exactly the week-one baseline and the week-two technical list that stage 6 assumes you already have, and the rest are the discovery and account-safety checks the off-site stage needs.
Sourced comparisons, practical guides and reference material. The comparisons are the one part of this site allowed to name other products, and every price in them carries the date it was checked rather than the date it was copied.
It is worth knowing the shape of the gap before you spend a week reading. Four claims you will meet constantly do not survive a check, and we do not teach them.
These systems change materially between measurements, and the change is bigger than most of the effects people report. One vendor time series shows a single engine's citation rate for one large forum falling from roughly 60% of responses to roughly 10% inside six weeks in 2025. Two peer-reviewed-track audits of Google AI Overviews put the share of cited domains absent from the organic first page at 53% (Kirsten et al., Findings of ACL 2026, 4,706 queries collected September 2025) and 29.8% (Xu, Iqbal & Montgomery, 55,393 queries collected March–April 2026) — the same construct, six months apart, and the later study is twelve times larger. Any figure in this field without a date attached to it is not worth reading. Everything here carries one, and everything older than about six months should be re-checked before you act on it.
Set a goal and Bavior runs the loop end-to-end — find, draft, post, verify, replan. You approve.
Tell Bavior the outcome — signups, feedback, visibility. It plans, executes, and replans until it's done.
Learn more →It scores real buying intent across your subreddits and backs every lead with evidence.
Learn more →Detail-rich, pitch-free drafts that follow each subreddit's rules and read like real participation.
Learn more →One-tap approval on every action, an independent reviewer, quotas, and stop rules keep accounts safe.
Learn more →A live scoreboard and weekly review track posts, replies, leads, and karma — and what changes next.
Learn more →Get alerted the moment Reddit talks about you or a competitor — and step in where it matters.
Learn more →Reddit content built to get cited by ChatGPT, Perplexity, and Google's AI answers.
Learn more →Every plan runs the full loop — plan, execute, verify, replan. You scale up the products, the volume, and the firepower.
GEO is a real but narrow specialisation that sits on top of SEO rather than replacing it. The retrieval layer under every major AI answer engine is a search index, and the largest controlled benchmark in the field found that moving a document to the top of the retrieved context — which is what classical ranking work does — was roughly 7.6 times more effective than the best content-rewriting tactic. What is genuinely new is the unit of optimisation (a passage rather than a page), the measurement problem (answers vary run to run), and the outcome (a mention that may never produce a click).
Stages 1 and 2 in order, then whichever of 3, 4 and 5 matches the gap your measurement found. That sequence exists for a specific reason: stage 2 produces the baseline and the citation log that tell you whether your problem is your own pages (stage 3 and 4) or somebody else's (stage 5). Starting with content is the single most common way a GEO program becomes unmeasurable, because the pages change before anyone recorded what the engines saw.
About one hundred minutes of reading across nine stages, and roughly two weeks of calendar time if you do the work each stage asks for. Stage 1 is reading. Stage 2 asks you to write a prompt panel, which takes an afternoon, and then to run it repeatedly, which takes a week of elapsed time because the point of that stage is that a single run is not a measurement. Stages 3 to 5 are work you schedule rather than finish. Stage 6 turns the whole thing into a 90-day plan.
Almost every large-sample dataset in this field is published by a company that sells AI-visibility software, and that company's product thesis requires AI search to be consequential and measurable. Those studies are often good, but they are not disinterested, and a curriculum built on them inherits their interest. So every outbound link in these nine stages goes to a peer-reviewed paper, a preprint labelled as such, an independent research institution, or an engine's own documentation. Where only vendor data exists for a fact, the page says so and states the confidence level instead of hiding it.
You can do stages 1 through 3 entirely by hand with a spreadsheet and a browser, and you should — running your own prompt panel manually for a week teaches you more about answer variance than any dashboard will. Tooling starts paying for itself inside stage 2, when you need repeated runs across several engines on a schedule, because that is the point where manual collection stops being reliable rather than merely tedious. Stage 4 needs access to your own server logs, which is a configuration question rather than a purchase.
The pipeline in stage 1 and the technical requirements in stage 4 apply everywhere, because they are properties of the systems rather than of a language. The published measurements do not: nearly every study cited here sampled US English queries, and the one multi-country study in the set found credible-source shares varying between 71.4% and 86.3% depending on assistant and topic. Treat the mechanisms as general and the percentages as English-language, US-market observations until somebody publishes otherwise.
What an autonomous Reddit agent is, and why it beats a bot or a tool.
Agency output, minus the retainer, the black box, and the lock-in.
Hand over the goal; get a weekly report of posts, replies, and leads.
Find buyer-intent threads and turn them into a qualified lead list.
Plan → execute → verify → replan, running around the clock.
How the categories compare — bots, reply tools, agencies, and agents.
The playbook: goals, subreddits, cadence, and what to measure.
A free tool to surface the communities where your buyers actually hang out.
Set a goal tonight. Wake up to a plan, drafts waiting for approval, and a report of what happened.