Learn how to build a metrics tree to align goals, track progress, and prioritize features that drive real product outcomes.
LLM QA isn’t just a tooling gap — it’s a fundamental shift in how we think about software reliability.
AI agrees too easily. That’s a problem. Learn how to prompt it to challenge your thinking and improve your product decisions.
AI governance is now a product feature. Learn how to embed trust, transparency, and compliance into your build cycles.
Rachel Bentley shares the importance of companies remaining transparent about reviews and where they’re sourced from to foster user trust.
Red-teaming reveals how AI fails at scale. Learn to embed adversarial testing into your sprints before your product becomes a headline.
Emmett Ryan shares how introducing agile processes at C.H. Robinson improved accuracy of project estimations and overall qualitative feedback.
Asma Syeda shares the importance of responsible AI and best practices for companies to ensure their AI technology remains ethical.
After designing AI search systems, I’ve seen what builds trust — and what kills it. Here’s my take on what really works.
Paul Weston talks about “quantifying the unquantifiable,” i.e., bringing in objective data for things that otherwise seem hard to measure.
Apple Intelligence is here. What does it mean for frontend dev and UX? Explore the core features of the update, do’s and don’ts for designing with Apple Intelligence in mind, and reflect on the future of AI design.
Adrienne Wang talks about how she’s learned to think creatively about products while also prioritizing scalable solutions.
