Just about every chat-based AI application kicks things off with a question. What do you want to do? Ask anything! They just assume that people always know what to ask for and how. But what if we start AI-powered experiences with an answer instead?
To illustrate, the Ask LukeW feature on my Website (which answers digital product design questions using my writings and files) could follow the examples above and start with a seemingly simple interface: just a greeting and an open text field.
While it looks simple, it can be anything but. People regularly struggle with knowing what to ask. To address this, Ask LukeW includes an always changing set of suggested questions to give people both a place to start and a sense of the kinds of questions they can ask. In usability testing, we consistently found that even just reading suggested questions helped people understand the site's purpose and how they could use it.
But even with suggested questions, people still have to ask a question to get started. After updating the retrieval system powering Ask LukeW, I realized things could be different. The new system supported time-based questions like: what did you write about recently? After seeing the results for this question a few times, I decided to just turn it into an answer.
Each day, I grab my most recent tweets, articles, and files and compile a "what's Luke thinking about now" answer automatically. That answer shows up above the suggested questions and gives people something to read and/or respond to without needing to ask anything.
It's a small change but one that addresses one of the most common AI product issues: capability awareness. In other words, how do I know what this product can do? It also aligns with a larger theme I've been poking at: skip the tools and make the outcome. In this case, starting with an answer vs. requiring a question.