GENZ2BOOMER
Overview
GenZ2Boomer was a rapid hackathon concept created in response to a challenge prompt focused on helping the newer generation learn how to navigate and blend into the current workforce.
Our team of three had only two hours to move from prompt to concept, define a product direction, and produce a tangible prototype. Early ideation explored several possible directions including AI-assisted message rewriting, real-time meeting feedback, workplace etiquette overlays, social decoding, and high-stakes conversation prep.
We ultimately focused on GenZ2Boomer, a workplace communication coach designed to help younger professionals translate casual or uncertain workplace communication into clearer, more professional, and more context-aware interactions without stripping away authenticity.
The final concept combined multiple connected experiences including a dashboard, an AI rewriter, a vibe-check tool, and mirror replay-style feedback for meetings and presentations. To move quickly, we used rough paper sketches to anchor the layout and feature set, then used ChatGPT, Gemini, UX Pilot, Lovable, and other AI tools to accelerate concept generation, screen direction, and coded prototyping.
Goals for This Project
The goal of this hackathon was to design an AI-powered product experience that could help younger professionals better understand workplace expectations, communicate with more confidence, and adapt to the norms of modern professional environments.
Because the challenge was time-boxed to just two hours, the team needed to quickly identify a problem space that felt meaningful, narrow the feature scope, and create a product direction that was both believable and demoable within the constraints of the sprint.
Another important goal was to show how AI could support workplace learning in a practical way. Rather than building a generic chatbot, we wanted to create a product that felt grounded in real moments of friction such as writing a message to a manager, preparing for a performance review, presenting in a meeting, or interpreting how tone might land with a coworker.
The concept also needed to show range. It had to feel like more than a single utility and instead suggest a broader product ecosystem that could support users before, during, and after workplace communication moments.
The Problem
Many younger professionals know what they want to say, but not always how to say it in a workplace context. Professional norms around tone, directness, diplomacy, and etiquette are often unspoken, and that makes it difficult for newer workers to build confidence, especially in higher-stakes situations.
Existing tools often focus on grammar or spelling, but they do not always help users interpret social nuance, understand how a message might land, or adjust tone based on context. This creates a gap between casual communication habits and the expectations of managers, clients, and cross-functional teams.
The challenge for this concept was to design something that could coach without feeling patronizing, support users without over-automating their voice, and create enough trust that people would feel comfortable using AI to improve how they communicate at work.
Research & Strategy
Given the two-hour hackathon format, this was not a deep research exercise in the traditional sense. Instead, the strategy relied on rapid synthesis of the host prompt, shared lived experiences, quick assumption mapping, and fast concept generation around common workplace communication pain points.
Our team generated multiple possible product directions, then evaluated them based on clarity of need, relevance to the prompt, ease of communicating the value proposition, and feasibility within a short prototype sprint. We chose GenZ2Boomer because it addressed a concrete user problem, offered a strong narrative for demoing, and allowed us to explore multiple connected AI-native features within one ecosystem.
We also used AI as part of the strategy process itself. Rough paper sketches helped define the structure, and then ChatGPT, Gemini, UX Pilot, Lovable, and other tools were used to expand concepts, shape screen directions, generate interface ideas, and accelerate the movement from abstract concept to tangible prototype.
Design Direction
The design direction focused on making workplace communication coaching feel approachable, practical, and embedded in realistic scenarios rather than abstract self-improvement advice.
The product was shaped as a small ecosystem of connected tools. The Rewriter helped users translate casual language into more professional communication. Vibe Check AI helped users gauge how a message might land before sending it. Mirror Replays introduced the idea of reviewing meeting or presentation behavior with AI-generated feedback. The Dashboard tied these experiences together through tone metrics, nudges, recent rewrites, and upcoming high-stakes events.
This direction allowed the concept to support the full communication lifecycle, from drafting and rewriting to reflection, practice, and ongoing improvement. It also made the product feel more like a credible platform than a single disconnected feature.
Impact
- Delivered an end-to-end product concept within a two-hour hackathon, moving from prompt to prototype under extreme time constraints.
- Narrowed a broad generational workplace challenge into a focused and understandable product concept with a clear value proposition.
- Created a connected feature ecosystem including a dashboard, rewriter, vibe-check flow, and mirror replay concept rather than stopping at a single-point solution.
- Demonstrated how AI tools can accelerate early-stage product discovery, interface concepting, and coded prototyping when paired with human judgment and prioritization.
- Produced a tangible prototype in Lovable that made the concept easier to communicate, critique, and evolve beyond the initial hackathon.
My Role
I was part of a three-person hackathon team responsible for taking the prompt from idea to working concept in just two hours.
My role included helping shape the product direction, define and prioritize features, translate rough ideas into a clearer UX structure, and use AI-assisted workflows to rapidly move from sketching to interface generation and prototype refinement.
Methodologies & Responsibilities
- Participated in rapid team ideation to generate and evaluate multiple concept directions before narrowing scope.
- Helped identify the strongest opportunity area by focusing on practical workplace communication problems faced by younger professionals.
- Used rough paper sketches to ground early layout and feature thinking before moving into digital generation workflows.
- Leveraged ChatGPT, Gemini, UX Pilot, Lovable, and other AI tools to accelerate concept development, prompts, screens, and prototype creation.
- Contributed to shaping the product ecosystem across the dashboard, rewriter, vibe-check, and mirror replay concepts.
- Helped refine interaction ideas and feature framing so the prototype told a coherent product story within a short demo window.
- Worked collaboratively within a compressed timeline to balance speed, clarity, and feature ambition.
Tools
AI-Assisted Ideation & Prototyping
ChatGPT, Gemini, UX Pilot, Lovable, and other generative AI tools
Concept Development
Paper sketches, rapid concept framing, AI prompt writing, feature prioritization
Collaboration
Real-time team collaboration during the two-hour hackathon sprint
Learnings
This project reinforced how quickly AI can compress the distance between concept and prototype, especially in a hackathon setting. Tools like ChatGPT, Gemini, UX Pilot, and Lovable helped the team move faster, but speed alone was not enough. Human judgment was still critical in choosing which idea to pursue, which features to prioritize, and how to make the final concept feel coherent.
Another major learning was that rough sketches still mattered. Even when AI tools were doing much of the heavy lifting in generation, the sketching process helped anchor the experience and gave the team a clearer point of view before prompting tools to create screens or code.
If this concept were taken further, the next step would be validating it with actual early-career users and managers, narrowing the MVP, and thinking more deeply about privacy, consent, and trust for any features that analyze messages, meetings, or ongoing communication patterns.
More Screenshots
