AI Interface Patterns: 15 Interaction Patterns for AI-Powered Products

AI products need interaction patterns for three jobs: asking the AI for help, reviewing what it produced and supervising what it does. The fifteen patterns below cover all three. Each entry says what the pattern is, when to use it, when not to and the main UX risk.

No product needs all fifteen. Choose by task: frequent small tasks suit inline patterns, open-ended questions suit conversation, consequential actions need approval and undo, and long tasks need background progress.

What: a text or voice box where people describe what they want in their own words. Use when: tasks are open-ended or users do not know where a feature lives. Avoid when: the task is frequent and well-defined; a button is faster than a sentence. Risk: the blank-box problem: users do not know what to ask or what the AI can do. Offer examples and scoped prompts. See AI chat interface design.

What: context-aware chips or buttons ('Summarize thread', 'Draft reply', 'Find similar orders'). Use when: the next likely steps are predictable from context. Avoid when: suggestions would be generic or crowd the interface. Risk: suggestions that are wrong for the context teach users to ignore them; measure usage and prune.

What: AI writes or completes content directly where the user is working (a field, a document, an email). Use when: the output belongs in that place and the user will edit it. Avoid when: the content is high-stakes and must be authored deliberately. Risk: users accept plausible text without reading; mark generated content until edited or accepted. See AI copilot UX.

What: an assistant panel beside the main workspace that knows the current context. Use when: users need help across many tasks without leaving the screen. Avoid when: it becomes the only AI entry point and sits unused beside the real work. Risk: 'chatbot in the corner' that adds little; connect it to actions on the page.

What: a compact card representing a delegated task with goal, status and next step. Use when: AI performs work that outlives a single message. Avoid when: the task completes instantly. Risk: cards with vague statuses ('working on it') that hide problems; use a small set of explicit states.

What: a structured request to approve, edit, reject or delegate a proposed action, with impact and evidence. Use when: actions are consequential or irreversible. Avoid when: actions are low-risk and undoable; approvals for everything cause fatigue. Risk: rubber-stamping. Show only what matters and make the decision fast. See AI action confirmation UX.

What: the AI produces a short form, pre-filled from context, for a one-off task ('Change delivery address'). Use when: the task needs structured input that a fixed screen does not cover well. Avoid when: a standard form already exists and is used often. Risk: missing or mislabelled fields; render from design-system components with schemas. See generative UI.

What: results returned as a sortable, filterable table with row actions instead of prose. Use when: answers involve several items with comparable attributes. Avoid when: there is one answer. Risk: numbers that look authoritative but came from the model; populate tables from tools and systems of record.

What: a short answer first, with detail, reasoning and sources available on demand. Use when: most users need the conclusion and some need the detail. Avoid when: a critical caveat would be hidden. Risk: burying limitations; keep essential warnings in the first layer. See AI transparency UX.

What: moving a task or conversation to a person with context. Use when: the AI reaches its limits or the user asks for a person. Avoid when: it is used to deflect rather than resolve. Risk: the user repeats everything. Pass a structured summary. See AI agent handoffs.

What: a clear way to reverse an AI action for a period. Use when: actions are reversible; undo often replaces confirmation. Avoid when: reversal is impossible (sent email, captured payment); confirm before acting instead. Risk: promising undo the system cannot deliver; say exactly what will be reversed.

What: a chronological list of what the AI did, with receipts and links. Use when: agents act on the user's behalf. Avoid when: the AI only answers questions. Risk: noisy logs nobody reads; summarize, group and highlight what needs attention. See AI agent trust UX.

What: citations, documents, records and data timestamps behind an answer or action. Use when: correctness matters and sources exist. Avoid when: the content is purely creative. Risk: citations that do not support the claim; link to the exact passage and test grounding.

What: a visible signal that the AI made an assumption, lacked data or found conflicting information. Use when: uncertainty is real and changes what the user should do. Avoid when: it would be decorative. Risk: uncalibrated confidence scores that mislead; prefer specific statements ('No price data after March') over percentages.

What: status for tasks that run while the user is away, with notifications for decisions and completion. Use when: tasks take minutes or longer. Avoid when: results arrive in seconds. Risk: notification overload or silent failure. See background AI agent UX.

For the architecture behind generated views, see generative UI; for interfaces inside ChatGPT and Claude, AI assistant app UX; for designing for agents as users, agent UX; for what changes when AI becomes the primary interface, AI-native vs AI-enabled software; for recovering from mistakes, AI error handling UX; and for choosing modalities, multimodal AI product design.

AI products are built from a small set of recurring interaction patterns. Use the ones that fit the task, respect each pattern's limits and design for its risks: blank boxes, rubber-stamped approvals, unread logs, misleading numbers and false confidence. Combined with a solid underlying product, these patterns let people ask, review and supervise AI with confidence. The broader principles are in AI UX design.