Summary: To build useful and usable AI-powered systems, our understanding of users’ needs and our design judgement must be encoded into well-defined evaluation criteria.
Design Decisions in Generative AI Systems
Imagine asking a large language model a question like “How’s the weather today?” The response might include too much information (“it’s 72 degrees, and it feels like 72 degrees with wind chill”) or too little ("It's nice out!"). It might say "It's unlikely to rain" when there's a 30% chance — technically below 50%, but high enough that most people would want to know. The AI is making design decisions about what to include in the response and how to phrase it. Without being able to specify every possible design decision the model might make, how do we influence these design decisions to be the “right” ones — the ones that serve users’ needs best, as grounded in research and our understanding of our target users?
The Shift from Deterministic to Probabilistic Systems
To answer this question, we can consider how design specifications are traditionally used when developing systems that are not AI-powered. Basically, our expectation as designers is that our engineering and QA partners will read our specs and write code that implements the exact behaviors we specify, including tests that validate that the code behaves as expected by the spec. Tools like Figma have simplified this process by allowing us to generate certain types of UI code and tests automatically, but this is the core model.
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