By playing, you agree to uListen's Terms and Privacy Policy and YouTube's Terms of Service.
How I AIGamma’s head of design on using AI to synthesize feedback and generate on-brand imagery | Zach Leach
Summaries, chapters, transcripts, and FAQs on this page are made by uListen, not YouTube.
At a glance
WHAT IT’S REALLY ABOUT
Gamma design leader uses AI for feedback synthesis and branding
- Zach Leach (Head of Design at Gamma) shows how he uses AI as a research assistant to digest large volumes of messy, multilingual product feedback—about 550 responses in a week—without manually translating or sampling only a small subset.
- He demonstrates using ChatGPT “deep research” to translate, summarize, and classify feedback by themes (what’s working, what isn’t, prompt patterns), then exporting those classifications for charts and team-ready reporting.
- On the brand side, he walks through a Midjourney-based workflow that uses style references/personalization to rapidly iterate toward consistent, art-directed imagery for UI “empty states,” plus Replicate models for fast background removal before dropping assets into Figma.
- Finally, he shares a lightweight Claude “project” workflow to standardize job descriptions across hiring managers, and reflects on what human designers should keep owning: fun, personality, and craft details.
IDEAS WORTH REMEMBERING
5 ideasDeep research turns unstructured feedback into usable product signals.
Instead of hand-reviewing a tiny sample, Zach uploads a feedback file and lets ChatGPT deep research translate, summarize, and identify what’s working/not working, including prompt-level patterns across many languages.
Quality of analysis improves when moving beyond keyword scripts.
Zach contrasts deep research with a prior approach: Python keyword matching (or row-by-row prompting). Deep research provided more nuanced classification and insight than basic text queries.
Feedback classification becomes a pipeline, not a one-off summary.
After deep research, he asks for per-row categorization and exports it into a spreadsheet to graph outcomes (e.g., which operations perform best/worst), enabling ongoing product tracking and prioritization.
Model tier differences show up in measurable satisfaction gaps.
Gamma compared paid vs free user feedback because different model access can change outcomes; they observed ~5% rating difference, reinforcing that model choice materially affects UX.
User complaints can directly inform UX “guardrails” for prompting.
A key issue was multi-step edits failing (the model completes only part of a compound request). Zach suggests UX responses like follow-up questions, automatic prompt splitting, or nudges toward step-by-step edits.
WORDS WORTH SAVING
5 quotesOver the course of a week, we got about 550 individual responses.
— Zach Leach
If I’m being totally honest, I probably would’ve hand-looked at maybe 20.
— Zach Leach
It sort of went through all the feedback, understood what’s working, what’s not working, what prompts work, what don’t work.
— Zach Leach
It’s almost like I can follow those rabbit holes of creativity.
— Zach Leach
I would hope that it’s never gonna be as good as making things fun.
— Zach Leach
High quality AI-generated summary created from speaker-labeled transcript.
