OpenAi+Setapp
OpenAI + Setapp
Introduction: Setapp, a platform offering a curated collection of Mac applications, sought to address user frustration with the laborious process of finding relevant apps. Users spent excessive time browsing through screenshots and descriptions, hindering app discovery. The idea emerged to simplify the user experience through an innovative solution.
Client
MacPaw
Services
Visual Design UI & UX Design AI
Industries
SaaS
Date
January 2023
Problem: Users faced challenges in efficiently locating desired apps, leading to frustration and decreased engagement. The existing app discovery process was time-consuming and lacked user-friendliness, impacting user satisfaction and platform retention. Solution: To streamline app discovery, we envisioned the Setapp Web Assistant, leveraging a conversational interface to guide users in finding apps based on features or use cases. The assistant would provide tailored recommendations, enhancing the user experience and promoting app engagement. My Role and Collaboration: As the Digital Product and Marketing Designer at Setapp, I spearheaded the development of the Setapp Web Assistant in collaboration with analysts, developers, and researchers. I facilitated communication between design and frontend teams, ensuring seamless integration of the assistant into the platform. Additionally, I sourced and processed data to train the assistant models, refining its capabilities through iterative testing. Approach: Our approach involved multiple iterations to fine-tune the Setapp Web Assistant's capabilities. Initially, we experimented with generating responses based on predefined prompts and app descriptions. Subsequent iterations incorporated advanced techniques, such as LangChain and embedding-based question answering, to enhance response accuracy and relevance. Results and Iterations: First Iteration: Initial attempts showed promising responses but required refinement due to hallucinations and formatting constraints. Second Iteration: Implementation of LangChain provided more coherent responses, yet token count inconsistencies raised concerns. Third Iteration: Leveraging embeddings and contextual chat information significantly improved response quality and relevance. Fourth Iteration: The final iteration focused on optimizing infrastructure to ensure efficient performance. Insights and Learnings: Chat-based models outperformed text-based ones, highlighting the importance of conversational interfaces. Techniques like "Putting word in GPT’s mouth" proved valuable for guiding user instructions effectively. Understanding and maintaining chat context improved user engagement and comprehension. Minimal infrastructure costs ensured cost-effectiveness in implementation.
Conclusion: The Setapp Web Assistant represents a significant milestone in enhancing app discovery for users, offering a seamless and intuitive interface for accessing relevant applications. Through iterative development and collaboration, we achieved a solution that addresses user needs effectively while maintaining cost efficiency. The insights gained from this project will inform future endeavors in digital product design, driving continued innovation and user-centric solutions.