Adaptive Interfaces: A/B Testing Contextual Prompts in a Writing App

AI · 6 min read

WriteFlow rolled out contextual prompts powered by a lightweight user model that inferred intent from document structure and recent edits. The product decision space included frequency of prompts, prompt placement, and opt-in granularity. Early experiments showed users appreciated help when stuck but reported annoyance when prompts were irrelevant or interruptive.

Designers tested three interfaces: inline subtle suggestions, a persistent suggestions rail, and a non-intrusive suggestions badge that opened a panel on demand. Each UI had different trigger heuristics—structural stasis, long pauses, or repeated deletions. The team also varied prompt wording to test directive versus suggestive tones and measured both short-term engagement and longer-term habit formation.

After six weeks, the badge-plus-panel model outperformed others on retention and task completion: it had a 27% higher acceptance rate for suggestions and fewer dismissal events. Qualitative interviews highlighted that users wanted control over when suggestions appeared and preferred contextual relevance over frequency. The experiment underlines the importance of adjustable autonomy: give users a low-friction gateway to AI help rather than forcing it into their flow.