LLM based search engine
How can we stand out in a competitive AI market by design strategies?
I solved the design problem with my team, which has five engineers, four marketers, two designers, and a contractor who helped finalize the project design.
[Project Overview]
In 2024, Alibaba International Group began developing the Xanswer model as a new AI-powered tool for general search targeting the global market. As a design intern, I worked on shaping the product strategy, gathering user insights, prototyping, and delivering visual designs from 0-1.
Industry
Automotive
My Role
UXUI Designer&Front End engineer
Tools
Figma three.js HTML CSS Blender
Timeline
January 2025- March 2025
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Research
Target User group
The product is targeted at users in the US. After identifying key industries like business, education, and research, we determined that the ideal user group would be individuals aged 25-45, as they are more likely to engage in these activities regularly.
Receiving the product requirements from the stakeholders
When I received the project, the stakeholders had already conducted extensive market research and shared their findings with me. My task was to consolidate this information, integrating key insights into the design process.
Ideation
Technical Feasibility (Engineering):
We collaborated with the engineering team to assess the technical feasibility of our key features, such as AI-generated search templates, mind map visualization, and seamless PPT integration, helping us prioritize the most impactful features.
Key User Insights
Design Phase 1
Key Prioritization
Before Lowfi prototype, we've prioritized key features based on user impact and technical feasibility. We've taken a comprehensive approach to ensure the highest value and short-term achievability. Then, I'll move on to the design phase.
Concept Design
I focused on creating a low-fidelity prototype and defining the information architecture (IA) to establish the core structure and functionality. I collaborated closely with the marketing team to confirm the layout of output results and functionality. We aligned on the core objectives of the product, discussing user needs, feature priorities, and how best to communicate the product’s value.
Internal Beta and Iterations
For internal testing of the low-fidelity prototype, we took a structured approach to gather feedback and validate core design elements. A team-wide review was conducted with designers, developers, and stakeholders to test basic user flows, navigation, and usability. I created test scenarios based on real user feedback, simulating typical user interactions.
Dark & Light Mode Design System
After the low-fi prototype, the contractor and I implemented responsive components for web and mobile, ensuring seamless support for dark and light modes. We developed an accessible color scheme for readability and contrast in both modes, using CSS variables for dynamic theme switching.
Launch Beta Version With Engineer
We conducted multiple rounds of pre-launch testing, resolving final bugs and inconsistencies to ensure a smooth user experience. After the beta launch, we used Google Clarity to collect real-time data on user behavior, tracking metrics like heatmaps, session recordings, and engagement. Based on these insights, I collaborated with the engineering team to make necessary adjustments, ensuring a polished, user-centered final product in Design Phase 2.
User insights based on 1.0 user feedback
We collected user feedback from comments on promotional posts across various online platforms. Users praised specific features, while also pointing out areas needing improvement.
Design Phase 2
Design Iteration
After collecting real user data from the beta version, I proceeded with additional design iterations. Once I confirmed the timeline with the front-end team, I implemented several key updates.
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