A high-fidelity user prototype is still a "smoke and mirrors" style simulation, however, now it looks very real. In fact, with many good high-fidelity user prototypes you need to look close to see that it’s not real. The data you see is very realistic, but it not real – mostly meaning it’s not live. For example, if I do a search for a particular type of mountain bike, it always comes back with the same set of mountain bikes, but if I look close, it’s not the actual bikes I asked for. And I notice that every time I search it’s always the same bikes no matter what price or style I specify. Now if you are trying to test the relevance of the search results this would not be the right tool for the job, but if you are trying to come up with a good overall shopping experience this is probably just fine and very quick and easy to do.

[...] The big disadvantage of a high-fidelity user prototype is that it’s not good for proving anything – like whether or not your product will actually sell. It is good for testing out usability, and it’s great for communicating the proposed product to key stakeholders, and it’s great for rapid learning. Where a lot of people go sideways is when they create a beautiful user prototype, and put it in front of 10 or 15 people that say they love it, and then they declare victory. Unfortunately, that’s not how it works. People say all kinds of things and then go do something different. We have a much better tool for proving whether something actually works (described next).

But my favorite use of a high-fidelity prototype is not to see if users like it, but rather, to try to learn why they don’t. When you test with a high-fidelity user prototype, you don’t get your answer from any one user, but every user you test with is like another piece of a puzzle, and eventually you see enough of the puzzle that you can see where you’ve gone wrong.