In this 4 minute video from my How AI Ate My Website talk, I illustrate how focusing on understanding the problem instead of starting with a solution can guide the design of conversational (AI-powered) interfaces. So they don't all have to look like chatbots.
But what if instead we could get closer to the way I'd answer your question in real life? That is, I'd go through all the things I've written or said on the topic, pull them together into a coherent reply, and even cite the sources, so you can go deeper, get more context, or just verify what I said.
In this case, part of my response to this question comes from a video of a presentation just like this one, but called Mind the Gap. If you select that presentation, you're taken to the point in the video where this topic comes up. Note the scrubber under the video player.
The summary, transcript, topics, speaker diarization, and more are all AI generated. More on that later, but essentially, this is what happens when a bunch of AI models effectively eat all the pieces of content that make up my site and spit out a very different interaction model.
Now the first question people have about this is how is this put together? But let's first look at what the experience is, and then dig into how it gets put together. When seeing this, some of you may be thinking, I ask a question, you respond with an answer.
Isn't that just a chatbot? Chatbot patterns are very familiar to all of us, because we spend way too much time in our messaging apps. The most common design layout of these apps is a series of alternating messages. I say something, someone replies, and on it goes. If a message is long, space for it grows in the UI, sometimes even taking up a full screen.
Perhaps unsurprisingly, it turns out this design pattern isn't optimal for iterative conversations with sets of documents, like we're dealing with here. In a recent set of usability studies of LLM-based chat experiences, the Nielsen-Norman group found a bunch of issues with this interaction pattern, in particular with people's need to scroll long conversation threads to find and extract relevant information. As they called out, this behavior is a significant point of friction, which we observed with all study participants.
To account for this, and a few additional considerations, we made use of a different interaction model, instead of the chatbot pattern. Through a series of design explorations, we iterated to something that looks a little bit more like this.
In this approach, previous question and answer pairs are collapsed, with a visible question and part of its answer. This enables quick scanning to find relevant content, so no more scrolling massive walls of text. Each question and answer pair can be expanded to see the full response, which as we saw earlier can run long due to the kinds of questions being asked.
Here's how things look on a large screen. The most recent question and answer is expanded by default, but you can quickly scan prior questions, find what you need, and then expand those as well. Net-net, this interaction works a little bit more like a FAQ pattern than a chatbot pattern, which kind of makes sense when you think about it. The Q&A process is pretty similar to a help FAQ. Have a question, get an answer.
It's a nice example of how starting with the problem space, not the solution, is useful. I bring this up because too often designers start the design process with something like a competitive audit, where they look at what other companies are doing and, whether intentionally or not, end up copying it, instead of letting the problem space guide the solution.
In this case, starting with understanding the problem versus looking at solutions got us to a more of a FAQ thing than a chatbot thing. So now we have an expandable conversational interface that collapses and extends to make finding relevant answers much easier.