This paper examines the role of post‑deployment user feedback in generative AI systems. Systematic feedback can reduce monitoring costs while boosting user engagement and trust. Regulations and industry guidelines mandate ongoing user input after launch, yet existing practices lack usability guidance, resulting in hard‑to‑find channels, ambiguous terminology, and little perceived user value.
We conducted a multi‑phase study in partnership with eBay. First, we benchmarked common industry feedback mechanisms and identified three core issues: (1) feedback entry points are hidden, making it difficult for users to initiate; (2) inconsistent terminology introduces noise; (3) the process does not respect user time, leading to low participation.
From these findings we derived best‑practice recommendations: embed feedback triggers within frequent interaction paths to improve discoverability; adopt concise, unified terms with examples to lower comprehension barriers; employ a tiered feedback structure that captures high‑level satisfaction first, then invites specific improvement suggestions, minimizing effort.
We then designed and prototyped a feedback‑collection tool. The tool uses a lightweight modal interface, offering a single‑click satisfaction rating, optional tags, and free‑text comments. All inputs are streamed to a product‑team dashboard that visualizes overall performance trends and hotspots. User testing showed a 35% reduction in interaction time and a 78% positive feedback rate.
In summary, a systematic, user‑centric feedback loop provides actionable insights for generative AI products and strengthens user trust.
Review