Match prototype fidelity to the risk you are trying to learn
Choose low or high fidelity prototypes for AI features based on the risk you need to reduce.
The principle: fidelity is a means to learn, not a prize to earn. Teams often overbuild prototypes because polished artifacts feel safer in review. But fidelity only matters when the details it adds change the decision you are trying to make. Low-fidelity work is ideal when you need to compare workflows, wording, or information architecture. High-fidelity work becomes worthwhile when microcopy, timing, motion, or realistic data context shape user judgment. AI products add a twist: many of the highest-risk questions are not visual at all. Users care whether the system used the right evidence, whether it shows uncertainty, whether they…
Sign up free — one personalized lesson every day, matched to your role and goals.
Already have an account? Sign in