Why generic onboarding loses users
Most products show every new user the same tour, regardless of whether they are a solo founder, an enterprise admin, or a curious browser. That one-size-fits-all approach guarantees that most users see steps irrelevant to them, and irrelevant steps are skipped — which is why the majority of churn happens in the first week, before anyone reaches value. AI-powered onboarding fixes this by turning a couple of sign-up answers into a personalised sequence of first actions. The user arrives at exactly the step that helps them, time-to-value drops, and early retention rises.
How it works
The flow has three parts. First, ask two or three high-signal questions at sign-up — typically role, primary goal, and experience level. Keep it short, because every extra question costs completion. Second, map those answers to recommended first steps. You can start with a simple rules engine and graduate to an LLM that reasons over the free-text combination of answers and returns an ordered list of tailored actions, phrased naturally. Third, measure and iterate: track time-to-value per segment, A/B test flow variants against a control, and feed the winning patterns back into your recommendation logic. The demo below takes a role, a goal, and an experience level and returns the kind of personalised onboarding plan an LLM step would generate.
Implementation notes
Run the personalisation live at sign-up — the user just handed you the inputs and expects an immediate next step — but cache the resulting plan so you do not re-call the model on every page load. Keep the model call server-side to protect your key, and always define a sensible default flow to fall back to if the call fails or times out. Instrument every step so you can see exactly where users drop, and define “value” concretely per product (first project created, first invite sent, first report generated) so time-to-value is measurable, not a vibe.
Tips and gotchas
Resist the urge to ask more questions “while you have their attention” — completion falls with each one, and an abandoned questionnaire personalises nothing. Only ask what would actually change the first step shown. When A/B testing, change one variable at a time and run to a real sample size rather than declaring a winner after a dozen users. Watch for personalisation that boxes users in: always leave an escape hatch to the full feature set so a mis-classified user is not trapped in a narrow flow. Finally, review the model’s recommendations against real activation data periodically — a plan that sounds good but does not move time-to-value needs revising.
Try the onboarding recommender
Pick a role, goal, and experience level to see a personalised first-step plan, the way an LLM-driven onboarding flow would generate it.