In Section 2 I argued that the old design process isn't producing great AI experiences - and that teams need new principles for building products people actually love. But when you sit down to design an AI feature, a practical question surfaces quickly: what patterns already exist? What are other teams doing to solve the blank canvas problem, build trust in AI outputs, or give users control over results? You don't have to start from zero.
Shape of AI (shapeof.ai) is a free, open pattern library created by Emily Campbell - VP of Design at Hackers/Founders - that catalogs how AI is reshaping interaction design. It documents the specific UX patterns emerging across AI products, organized into categories that map directly to the design challenges teams face when building AI-powered experiences.
What it does
Shape of AI organizes AI interaction patterns into six categories. Each one addresses a different moment in the user's relationship with AI:
1. Wayfinders help users get started. This is where many AI products fail - the blank text box with no guidance. The patterns here include suggestion chips, example galleries, templates, nudges, and randomize options. If you've ever opened an AI tool, stared at an empty prompt field, and didn't know what to type, a wayfinder was missing.
2. Prompt Actions are the different things users can direct AI to do - summarize, expand, restyle, transform between formats, synthesize across sources, or regenerate. These patterns help teams think beyond the default chat interface and design specific, purposeful interactions.
3. Tuners let users refine and shape results. Attachments, filters, connectors to external data, model selection, modes, preset styles, and voice and tone controls all fall here. This is where user agency lives - the ability to say "not like that, like this" without starting over.
4. Governors maintain human oversight. This includes citations, action plans (showing the AI's steps before it executes), verification prompts, branching, controls to pause or redirect mid-stream, cost estimates, and stream-of-thought displays. If you covered Human-in-the-Loop in Issue #2 of this newsletter — this is what it looks like in practice at the interface level.
5. Trust Builders help users believe the output. Caveats about model limitations, data ownership controls, disclosure labels, footprints that trace how the AI reached its answer, and incognito modes. These patterns are especially critical for enterprise use cases where accuracy and accountability matter.
6. Identifiers are the brand-level choices that make an AI experience recognizable - avatars, color systems, iconography, naming conventions, and personality definition. These seem cosmetic but they shape how users perceive and relate to the AI.
How I use it
When working with clients on AI features or when building my own AI products, I use Shape of AI in two ways. First, as a diagnostic tool. If an AI feature has low adoption, I walk through the categories and check which patterns are missing. Almost always, the issue is in Wayfinders (users don't know how to start), Governors (users don't trust the output), or Tuners (users can't shape the result to their needs). The categories give you a structured way to find the gap without guessing.
Second, as a design reference in workshops. When a team is designing a new AI-powered experience - say a customer support assistant or an internal knowledge tool - I use the pattern categories as a checklist: How does the user get started? What actions can they take? How do they refine results? How do they stay in control? How do they know they can trust this? Going through these questions before building prevents the most common AI UX failures.
The site also includes a UI Library with real-world screenshots from products like Perplexity, Claude, Notion, Midjourney, Slack, and others - so you can see how these patterns are actually implemented, not just described.
What I like about it
Shape of AI fills a gap that didn't have a good answer until recently. There are plenty of resources on how to build AI models, how to prompt them, and how to evaluate their performance. But there's very little practical guidance on how to design the interaction layer - the part the user actually experiences. Emily's library provides a shared vocabulary for teams that are designing AI features, which is especially valuable when designers, product managers, and engineers need to align on what "a good AI experience" actually means.
It's also free, open (Creative Commons licensed).
For the full context behind why these patterns matter and how AI is reshaping interaction design, watch Emily Campbell's talk from Hatch Conference 2024 (which surprisingely still apply): "The Shape of AI: How AI is Reshaping Interaction Design." She traces the history from mainframes to algorithms to AI, and makes the case that AI can give agency back to users - if we design it that way. Her 10 Heuristics of AI - from "Purposeful and Needful" to "Identification and Honesty" - are a useful companion framework to the pattern library itself.