Overview
At ZipRecruiter, we discovered that many job seekers struggled to identify the right career paths. To tackle this, we developed an LLM-powered career recommendation tool that engages users in a conversation to uncover their preferences and potential. The tool provides personalized job title suggestions, helping users navigate their career options more effectively.
As the design lead, I led the development of this AI-driven conversational chat interface, collaborating closely with the VP of Product Design, the product designer, and cross-functional teams, including user research, data science, engineering, and marketing.
The tool had a significant impact, achieving 92% user satisfaction and an 85% conversion rate to registration.
Role
UX/UI design
Data review & analysis
Wireframing
Prototyping
Timeline
Nov 2023 — Feb 2024
Challenge
Research and Insights
Surveys
From 120 job seekers, 75% felt overwhelmed by unfocused results, and 68% struggled to identify suitable roles.
User Interviews
Behavioral Data Analysis
60% of users started their searches with a blank query, and 42% abandoned their searches within minutes.
Goals
Empower users
Connect users' skills and goals with relevant career opportunities.
Boost engagement
Create dynamic, interactive flows that adapt to user inputs.
Drive conversions
Build trust early by delivering immediate and tangible value.
Design Iteration
Structured Wizard Flow
Pros
Familiar and straightforward; ensured all required data points were collected.
Cons
Felt rigid for users without conventional credentials; limited flexibility and engagement.
Why we moved on
While clear and structured, the design didn’t accommodate diverse user needs, prompting us to explore more flexible options.
Free-text Input
Pros
Allowed users the freedom to share their story; provided richer, more personalized insights.
Cons
Users might feel overwhelmed by the open-ended nature; some hesitated to share personal data with AI.
Why we recommended
We believed this approach had the potential to unlock deeper user insights. However, concerns about usability led us to validate this design through testing.
To make an informed decision, we conducted sandbox testing with the free-text input design, collecting 800+ user responses in a single day.
Sandbox testing screen
We learned that users valued the flexibility and were eager to share detailed information about themselves. Many were excited by the idea of AI helping them find career options. However, feedback revealed a desire for more guidance to reduce overwhelm and indecision. Building on the insights from the smoke test, we developed a conversational, chat-style interface that combined structured and open-ended interactions.
Final Design Approach
An AI-powered chat interface that feels natural and intuitive, beginning with "Tell me about yourself" to encourage users to freely share their aspirations.
Questions evolve dynamically based on user input, with each response shaping the next question to create a more personalized experience.
Beyond exploring skills and interests, the tool gathers key preferences such as salary expectations to offer more tailored recommendations.
If the initial recommendations don’t meet users' expectations, additional questions are asked to improve the recommendations.
Each career recommendation includes brief descriptions and explanations, highlighting how the suggestion aligns with the user’s background and preferences.
Results
of users satisfied with results
conversion rate to registration
lift in Monthly Active User
Post MVP
Next project