PostHog onboarding prompt capture for AI attribution
When a user says AI sent them, ask for the exact prompt in onboarding so the growth team learns from real buyer language instead of synthetic guesses.
Why this can grow a startup
Most AEO reporting stops at vague channel attribution. That misses the thing that actually helps the next content decision: the sentence the buyer typed. PostHog solved that by adding a conditional onboarding question for AI-sourced signups and collecting the literal prompt. That is stronger than third-party estimates because it comes from a person who converted, not from a tool simulating intent. The downstream value is broad. The team gets phrasing to reuse in pages, better prompt sets for visibility tracking, and early warning when the product is being discovered for reasons it did not expect. It also costs almost nothing compared with buying yet another reporting product.
Source-backed example
PostHog added a conditional onboarding question asking AI-sourced signups which prompt they used and says it collected 9,214 real prompts in its first three months, with a daily Slack report of 50-100+ examples.
Source-reported evidence
Source: PostHog: LLMs are picking winners. Here's how to become one. · Browse PostHog: LLMs are picking winners. Here's how to become one. tactics
PostHog captured 9,214 real AI-origin prompts in three months and now reviews 50-100+ new prompt examples in Slack each morning.
Attributed to the cited source; GrowthDex has not independently verified it. Use this as context, not as a forecast, benchmark, or recommended target.
Evidence scope: claims and metrics are attributed to the linked source; GrowthDex has not independently replicated the reported result. Check the original context and validate fit before applying it. Read the evidence methodology.
When to use it
Use this when AI visibility, Onboarding, Attribution is relevant to self-reported attribution, prompt capture, onboarding survey and you can run a bounded test with a free budget.
When not to use it
Do not use it as a substitute for customer evidence, a clear owner, or a measurable stop condition. Local platform rules and market behavior still need checking.
How to do it
- Choose one target segment and define the exact next action this tactic should prompt.
- Adapt the tactic to AI visibility and Onboarding, using evidence from PostHog: LLMs are picking winners. Here's how to become one. as context—not as a promised result.
- Record a baseline for: Share of target users completing the product's first-value action. Set a guardrail that would make you pause the test.
- Run a narrow pilot and choose a test window that fits the normal decision or use cycle; do not scale before you have observed the full cycle.
- Compare the signal with its baseline. Scale only if the intended action improves without crossing the guardrail; otherwise revise one variable or stop and record what you learned.
Compare a stack for your situation
Compare source-backed tactics against your product, stage, budget and channel strengths, then choose one experiment to test.
Explore the context
Did you launch this experiment?
Report a launch to help us understand which growth ideas founders put into practice.
Apply this with an operator
Turn isolated search tactics into a crawlable visibility system tied to demand and proof.