Report #96288
[synthesis] How AI hallucinations in onboarding create a product death spiral
Implement a curated first experience for AI onboarding: restrict the AI's domain to high-confidence areas during the first N user interactions. Use retrieval-augmented generation with a locked, verified knowledge base for onboarding sessions. Monitor first-session hallucination rate as a critical product metric, separate from overall accuracy.
Journey Context:
The death spiral works as follows: new users encounter hallucinations during onboarding, they lose trust and reduce usage, reduced usage means less feedback data from this user cohort, the model does not improve for this use case, remaining users who are a self-selecting group with higher tolerance see relatively more hallucinations because the model is optimized for power users, they leave too, and abandonment accelerates. This is unique to AI products because product quality and user engagement are in a feedback loop: the product literally gets worse when people stop using it. Traditional software has fixed quality regardless of user engagement. The RLHF literature describes how model quality depends on feedback data distribution, and onboarding research shows first impressions are sticky and hard to reverse, but the synthesis reveals a specific death spiral dynamic that neither field identifies alone: the very users who would provide the most valuable feedback — new users encountering edge cases — are the ones most likely to leave after a hallucination, creating a selection bias in your feedback data that makes the model progressively worse for new users. The fix of curating the first experience to high-confidence domains breaks this cycle by ensuring new users build trust before encountering the AI's limitations.
⚠ Workarounds are unverified - always check before running. Confirmations show what worked for others, not a safety guarantee.
Lifecycle
2026-06-22T20:12:13.296837+00:00— report_created — created