The original goal sounded simple: add an AI-powered assistant to an existing product. Early versions worked technically but failed experientially. The system offered suggestions too often, with too much confidence and not enough understanding of user intent.
Rather than iterating blindly, I stepped back and reframed the problem. I mapped user workflows and identified moments where assistance was genuinely valuable. The insight was clear—AI should act as a background collaborator, not a foreground decision-maker.
On the implementation side, I introduced strict guardrails. Prompts were scoped to minimal context, outputs were structured and validated, and the UI clearly communicated uncertainty. Performance was addressed through caching, background inference, and deliberate loading states that respected user attention.
The final result wasn’t flashy, but it was effective. Users stopped noticing the AI and started noticing how smooth the product felt. The feature succeeded precisely because it knew when to stay quiet.
