The rise of AI UX: how to design AI assistants that users will love
Imagine having an assistant who could chat with you to help get work done, answer questions, or brainstorm ideas. We are all familiar with ChatGPT, but what if its smarts could be directly embedded in our workflow? AI "copilots" are making this a reality. But designing great copilot experiences takes way more thoughtfulness and care than I initially thought.
A few weeks ago, I stumbled on a great video on AI user experience (UX) design from the Microsoft Build conference. Rachel Shepard , Director of Design for AI Platform, and Kurtis Beavers , Fluent AI Design Director, discussed designing for Copilot - Microsoft’s name for embedded AI assistants surfacing throughout their products. I highly recommend that you check out the whole video.
As design leaders for Microsoft AI products, they’ve seen firsthand how to create copilots, or AI assistants, with quality UX. In this post, I’ll share some of their insider tips on crafting a copilot, from managing expectations to continuous improvement. Deceptively simple, there is so much to be thought through that I believe that AI UX will blossom into a whole specialty unto itself.
Defining Copilot
To start, what exactly is a copilot? Essentially, an AI agent collaborates with you on tasks through natural conversation. Under the hood, copilots leverage large language models like GPT-4 to generate responses based on their vast training data and augment it with your application's data. But to users, it just feels like chatting with a helpful assistant.?
Compared to traditional apps, copilot interactions are more unpredictable, like a choose-your-own-adventure story. Users drive personalized experiences based on their questions and requests. Designers have to embrace this ambiguity rather than try to control rigid paths. In the video, Rachel calls for a probabilistic mindset attuned to likely outcomes, not prescripted certainties.
Laying the Foundation?
The Microsoft Fluent 2 Design System provides the foundation when constructing a copilot, at least for Microsoft products. They have expanded it beyond the typical focus on visual components to include education, conversation norms, trust building, and, crucially, prompt design.?Their core AI UX principles include:
Framing the experience properly is critical so users understand the technology’s current stage. We are coaching users on collaborating effectively rather than setting sky-high expectations. The proper framing shapes how people perceive the system’s strengths and limitations.
Copilot Altitude
Copilots can engage with users across different modalities, or "altitudes," as they call them. Full-screen chat interfaces for free-form dialogue are on one end of the spectrum ("above"). This works well for open-ended queries or back-and-forth brainstorming.
In other cases, a side-panel copilot (“beside”) might monitor the core app experience and interactively assist users. Copilots can also be embedded "inside" specific UI elements for focused micro-tasks to provide just-in-time guidance.
Determining the appropriate modality depends on the use case and context. Sidebars suit complex workflows by advising on app functionality. Embedded copilots help with precise steps. Chat excels for discovery and exploration. Mixing modalities allows each interaction to feel fluid and natural.
The Deceptive Simplicity of Copilot UI??
Copilot UIs look straightforward on the surface. Behind the minimalist interfaces, though, designers must craft details thoughtfully—elements like previews, citations, and notifications.?
领英推荐
The teams at Microsoft spend lots of time working through nuanced functionality around latency, accuracy, grounding in source materials, and more. How might they balance a snappy response time while delivering an in-depth quality result? What sources or data should responses link to so users can verify credibility? How do they indicate when a copilot hallucinates an answer rather than pulling from reliable information?
Every design choice aims to build users’ trust at the appropriate level. While copilots can generate helpful information, they sometimes fabricate or hallucinate content that requires correction. UI tweaks help flag these issues, allowing people to steer toward truthful insights.
The Art of Prompt Engineering??
Prompt engineering takes experimentation and patience. You could compare it to perfecting a recipe. The ingredients and instructions must precisely guide the AI to serve the knowledge you’re hungry for. Going too broad or too vague produces mushy meaningless results. Overly narrow or complex prompts also risk failure.?
Start by writing prompts that clearly describe the problem to be solved or the question to be answered. Build in guardrails to bound the scope if needed. Provide examples that illustrate the desired tone and depth. Work iteratively, tweaking prompts based on the outputs generated until you consistently get delicious results.?
Lately, I have been successfully expanding my often simplistic prompts into much more detailed instructions for the language model. A well-crafted prompt makes a massive difference to the output from an LLM, and there are many techniques you can use to improve them.
The People Powering Copilots
Copilots demand increased collaboration between groups that previously didn’t intersect much, like design, research, engineering, content strategy, and trust and safety teams.?
This rallying of different experts unlocks creativity and checks potential pitfalls. Designers ground lofty AI ambitions in realistic user needs. Engineers advise on technical constraints that enable seamless experiences. Researchers supply data-driven insights and identify pain points. Prompt engineers finesse conversation flow. Ethicists monitor for potential harm. The combined superpowers drive breakthroughs.
Developing AI responsibly requires this collaborative spirit. Copilots augment human capabilities rather than replace them. So designing copilots together makes the output more insightful than any individual could produce alone.
Launching and Iterating?
Shipping copilots requires a build-measure-learn approach. Since you can’t fully predict how users will converse, regularly releasing minimum viable products lets you gather feedback and continuously refine.?
Plan ahead for how improvements will integrate back into the copilot post-launch. Monitor both qualitative signals like user delight as well as usage metrics. Be ready to update content and prompts based on common queries. Expand capabilities over time as the models advance.?
Occasionally, you may decide certain features are no longer needed if the copilot proves adept at handling associated tasks. It takes humility to recognize when technology evolves past previous solutions. But minimizing duplication avoids clutter and complexity.
The Future of AI Assistance???
It’s an exciting time to think about how AI assistants integrate into our lives. We will continue dreaming up ways to combine human strengths with intelligent machines. AI can help us achieve our goals while avoiding drudgery. Approached thoughtfully, it will enhance how we learn new skills, complete workflows, run our businesses, and more.?
I can't wait to see how "AI UX" designers build out the patterns and frameworks we will need to be successful. We are still in the Geocities era of AI.