🔮 AI Interaction Design Patterns (https://www.shapeof.ai), a fantastic (!) living catalog of emerging design patterns, heuristics, anti-patterns and real-life examples that shape the experience of AI — from identifiers and wayfinding to prompts, tuners and trust indicators. Incredible project by incredible Emily Campbell. 👏🏼 👏🏽 👏🏾

AI experience can go way beyond ChatGPT. One of the most underrated yet impactful patterns for AI interfaces is the ability to tune AI experience. This could show itself as a style lenses or temperature knobs — little tools to help users generate a more personalized output easier. E.g. Sad ↔ Happy, Concrete ↔ Abstract, Creative ↔ Precise.

Instead of expecting large and highly detailed text prompts, we could help users articulate their intent with prompt presets, templates or constructors. Perhaps by defining a expected format, structure, personas, roles as checkboxes or chips — both for user input and AI responses (priming).

Another much-needed feature is scoping. Users should be able to quickly scope their inquiry to a particular domain, source or even a set of videos or PDFs. That’s one of the features that would make output much more specific without having to write a long prompt.

And: the AI output shouldn’t be bulky nor static. Users should be able to granularly iterate or revise little bits of it — e.g. by asking for sources of specific statements, or diverging from one view to another, or manipulating small parts of an image or a video. These refinements should happen not with text prompts, but contextually — on the relevant parts of AI outcome.

We can go way beyond a text prompt alone. Better results come from combining good old-fashioned design patterns such as search or filtering with AI — to first find relevant and trustworthy sources, and then generate insights from them. That’s a great way to boost accuracy and make AI more relevant to more people.

💎 Design Patterns For AI Interfaces

Design Patterns For Building Trust, by If
https://lnkd.in/eEJngtVv

AI Design Patterns Catalogue, by Maggie Appleton
https://lnkd.in/ebAp9Sb8

Style Lenses For AI, by Amelia Wattenberger
https://lnkd.in/e-SJis23

AI Search UX Patterns, by Daley Wilhelm
https://lnkd.in/eMZAayb7

Rise Of The AI Model Designer, by Paz Perez
https://lnkd.in/eb7SHcVz

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🚀 Fantastic AI Examples:

Scispace (search + AI): https://lnkd.in/etYERK4u change
Scopus (confidence levels): https://lnkd.in/eSqznt7U
Dream Machine (inline action): https://lnkd.in/et8Ss9KM
v7 Labs (AI auto-fill): https://v7labs.com/
Miro (AI presets): https://miro.com/ai/
NotebookLM (scoping): https://notebooklm.google/
Claude (shareable URLs): https://claude.ai/new
Perplexity (search + AI): https://www.perplexity.ai/
Gemini (refinement): https://lnkd.in/ebvdNXU9

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hashtag#ux hashtag#ai

AI interactions are evolving fast, and it’s great to see smarter design patterns that make AI more useful and intuitive. Features like tuning responses, scoping information, and refining outputs right where you need them could make a huge difference. At Micro Bully Guide, we know how important it is to get clear, reliable info without the hassle. Applying these AI principles could make finding the best insights on Micro Bullies even easier. Love seeing projects like Shape of AI pushing things forward: https://microbullyguide.com/