Maven - Design Patterns For Ai Products In 2026
 

How to design AI products that people actually use.
As product teams rush in launching AI features, they quickly face a roadblock. Too often AI tools have very low adoption and retention, they are painfully slow and unreliable, responses are walls of text and users have to ping-pong between prompts, over and over again. Let's fix that!
In this course, you'll learn
100s of real-life examples and UX guidelines
Practical design patterns for AI products
Recent UX research on AI UX and accessibility
How to build trust and confidence for AI
How to capture and design for user's intent
How companies design AI features, where they fail and succeed
Everything senior designers must know to design effective AI experiences
No fluff, no theory - just what actually works in real products. Expect a practical journey with actionable design guidelines - from efficient prompting UX to effective output and refinement journeys, along with frequent UX and accessibility blockers.
The workshop includes
? Real case studies from real products
? All video recordings, slides, resources, templates
? Hands-on tasks and actionable insights
? Live interactive sessions and feedback
? Dedicated time to answer all your questions
? Well-deserved certificate for your hard work
What you'll learn
You'll learn how to design AI features that people understand, use and trust - with practical UX guidelines and tons of real-life examples.
State of AI UX in 2026
New UX research on how people discover and use AI features, and why many AI features have poor adoption and poor retention rates
How people work with and around AI features, with main slowdowns, blockers, usability and (often overlooked) severe accessibility issues.
High interaction cost of prompt engineering, context awareness, capability awareness and AI discoverability
Design Patterns For AI Products
Quiet AI vs. Visible AI, prompt strength indicator, daemons, modifiers, task builder, forced ranking, style lenses, precision knobs.
Design patterns for building trust, navigating AI output, proactive AI, presets, pre-prompts, capability awareness, context engineering.
Interaction design patterns, accessibility issues, how to capture and design for user's intent, with case studies and examples
Boosting AI UX In Complex Products
Where AI typically lives and how to help people provide better input, make sense of AI output and refine it to match user's needs well.
How to support decision-making and complex workflows - with guardrails, permissions, approval flows and human in the loop.
Why linear customer journeys often don't map well with AI features, and how we need to change our process to design AI loops instead.
Designing For Trust and Confidence
Design strategies that build confidence and clarity around AI behaviors and decisions.
How transparency, better reasoning traces, consensus meters and transparency increase retention of AI features
How to signal and label AI-generated content, and make it work with human-written, curated content
Real-Life Case Studies
How to design AI features and experiences from scratch - with AI Design Canvas, from data collection to gathering feedback
How companies design and implement AI features in their products, where they fail and succeed
Why linear customer journeys often don't map well with AI features, and how we need to change our process to design AI loops instead.
AI Design Workflow, From Start to Finish
How to bring AI into the product, from data collection and data cleansing to designing AI loops and user flows.
Agentic UX - how to support decision-making and complex workflows - with guardrails, permissions, approvals and human in the loop
How teams measure the quality of AI UX and how valuable and reliable AI features actually are for end users
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