21 GenAI UX Patterns, Examples & Implementation Tactics
Generative AI introduces a new way for humans to interact with systems by focusing on intent-based outcome specification. GenAI introduces novel challenges because its outputs are probabilistic, requiring understanding of variability, memory, errors, hallucinations, and malicious use.
This brings an essential need to build principles and design patterns, as described by IBM. Moreover, any AI product is a layered system where the LLM is just one ingredient, and memory, orchestration, tool extensions, UX, and agentic user-flows build the real magic!
Below is a concise definition and example for each of the 21 GenAI UX patterns:
- GenAI or no GenAI Definition: Evaluate whether GenAI improves user experience or introduces complexity. Example: Intercom’s FinAI captures user intent and provides detailed explanations, unlike a structured button-based chatbot or FAQs list.
- Convert User Needs to Data Needs Definition: Translate user goals into structured data inputs to train AI models effectively. Example: Paper "Towards Accountability for Machine Learning Datasets" outlines best practices for the data development cycle in software engineering.
- Augment vs Automate Definition: Decide whether to enhance human capabilities or fully automate tasks based on user intent and preference. Example: Magenta Studio in Ableton gives creative controls to manipulate music without taking over the process.
- Define Level of Automation Definition: Determine the degree of automation (no, partial, or full) based on user pain points and risk levels. Example: GitHub Copilot suggests code based on context but allows users to edit, accept, or ignore suggestions.
- Progressive GenAI Adoption Definition: Gradually onboard users by highlighting benefits, simplifying onboarding, and building trust through explainability. Example: Adobe FireFly progressively introduces basic to advanced AI features to users.
- Leverage Mental Models Definition: Align AI interfaces with existing user mental models to ensure intuitive interactions. Example: Generative Expand in Photoshop builds on the familiar idea of expanding an image.
- Convey Product Limits Definition: Clearly communicate what an AI system can and cannot do to set realistic expectations. Example: Claude AI assistants clearly state their knowledge cutoff dates or when they're unsure about an answer.
- Display Chain of Thought (CoT) Definition: Show how AI arrives at conclusions to improve transparency and foster trust. Example: Perplexity AI enhances transparency by displaying its processing steps.
- Leverage Multiple Outputs Definition: Present varied AI responses to allow users to explore and refine options. Example: Google Gemini provides multiple options to help users explore and refine decisions.
- Provide Data Sources Definition: Attribute AI outputs to credible sources to enhance transparency and credibility. Example: Google NotebookLM adds citations automatically, linking answers to specific parts of user documents.
- Convey Model Confidence Definition: Indicate AI confidence levels to help users assess reliability. Example: Grammarly app uses verbal qualifiers like "likely" to indicate confidence in its content.
- Design for Memory and Recall Definition: Enable AI systems to remember past interactions for better personalization and context awareness. Example: ChatGPT remembers key facts shared by users to personalize future chats.
- Provide Contextual Input Parameters Definition: Tailor inputs based on user preferences and past interactions to streamline experiences. Example: ElevenLabs lets you adjust settings to design a custom voice.
- Design for Co-Pilot/Co-Editing/Partial Automation Definition: Allow AI to assist users while retaining human control over final decisions. Example: Notion AI helps draft, summarize, and edit content while the user retains final control.
- Design User Controls for Automation Definition: Offer user controls to manage or override AI automation based on context and needs. Example: Gmail users can enable or disable predictive text and auto-replies.
- Design for User Input Error States Definition: Handle ambiguous or incomplete inputs gracefully to maintain trust. Example: ChatGPT asks follow-up questions when given vague prompts like "What’s the capital?"
- Design for AI System Error States Definition: Address AI errors transparently, providing recovery options and user agency. Example: Citibank’s financial fraud system displays error messages due to false positives/negatives.
- Design to Capture User Feedback Definition: Collect feedback to continuously improve AI models and align them with user needs. Example: ChatGPT uses reaction buttons and comment boxes to collect user feedback.
- Design for Model Evaluation Definition: Continuously assess AI performance using automated, code-based, and human evaluations. Example: Amazon Bedrock uses LLM-as-a-judge for model evaluation.
- Design for AI Guardrails Definition: Implement practices to minimize harm, misinformation, and biases while ensuring ethical compliance. Example: Instagram uses hybrid approaches to spot toxic content and misinformation.
- Communicate Data Privacy and Controls Definition: Clearly explain how user data is handled to build trust and empower users. Example: Slack AI communicates that customer data remains owned and controlled by the customer.
Let me know if you'd like further elaboration or additional examples!
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