Designing AI products you can depend on

This post shifts the focus from AI as a feature to AI as a design material, emphasizing the importance of product design around AI models to build dependable AI products. It introduces design levers such as inference latency, model size vs. performance, explainability, traceability, and robustness. Key principles for collaborative AI are outlined: exposing intent before output, designing for decomposition, surfacing uncertainty, and assuming human participation. The role of language and content design in shaping AI behavior is highlighted, along with the concept of bounding AI creativity based on task sensitivity and tolerance for variance. Measurement strategies are updated to focus on user experience signals like output structure, tone consistency, verbosity, and correction cost. The post concludes by emphasizing that dependability is designed, not assumed, and is earned through intentional product choices that keep humans in the loop.