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Prototyping

1 post about Prototyping. Every summary links to the original.

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Intercom
Intercom on Product: How to design standout products in an AI world - The Intercom Blog
Intercom’s VP of Design Emmet Connolly and Staff Product Designer Molly Mahar discuss how product designers must adapt their methods for AI, drawing from their experiences building Intercom’s products. They explain that designing for AI means moving from deterministic to probabilistic systems, where unpredictability is the norm and designers must design flexibly around surprises. Data quality is foundational for reliable AI, so designers should prioritize it; realistic prototyping using actual data helps capture essential user feedback and evaluate output effectiveness. Extensive testing is necessary to anticipate possible system failures; if live versions catch designers off guard, testing must be more robust. Building user trust requires consistent positive experiences and transparency about how the system works and its data, enabling designers to create AI experiences that make users feel comfortable and confident.

The best engineering reads, once a week.

Topic

1 post about Prototyping. Every summary links to the original.

Paid picks we use ourselves. Links carry a referral code — a subscription through one earns us a commission at no extra cost to you.

Newest first

Intercom
Intercom on Product: How to design standout products in an AI world - The Intercom Blog
Intercom’s VP of Design Emmet Connolly and Staff Product Designer Molly Mahar discuss how product designers must adapt their methods for AI, drawing from their experiences building Intercom’s products. They explain that designing for AI means moving from deterministic to probabilistic systems, where unpredictability is the norm and designers must design flexibly around surprises. Data quality is foundational for reliable AI, so designers should prioritize it; realistic prototyping using actual data helps capture essential user feedback and evaluate output effectiveness. Extensive testing is necessary to anticipate possible system failures; if live versions catch designers off guard, testing must be more robust. Building user trust requires consistent positive experiences and transparency about how the system works and its data, enabling designers to create AI experiences that make users feel comfortable and confident.

The best engineering reads, once a week.