📐 Design Considerations

Discoverability & Learning

• Can users discover AI abilities without instruction?

  Make AI capabilities visible and understandable.

• Are advanced features revealed as users gain proficiency?

  Gradually expose advanced controls through progressive disclosure.

• Does the system guide users to explore AI capabilities?

  Encourage guided exploration through examples and contextual suggestions.

Control & Transparency

• Can users steer or constrain what AI does?

  Provide steering controls to refine and limit AI actions.

• Is it clear how to provide, manage, and remain aware of what context AI has?

  Provide clear and intuitive interaction elements for context management and visibility.

• Is it clear how the AI arrived at its outputs?

  Use transparency patterns to explain AI reasoning and outputs.

• Can users override AI-initiated actions when needed?

  Offer override mechanisms for canceling or adjusting AI actions.

• Are blended input methods available to suit different needs?

  Support a mix of conversational prompts and embedded UI controls.

Interaction Scope

• Is the interaction tightly scoped or open-ended?

  Align scope with user intent. Use constrained UI for efficiency, open-ended modes for exploration.

• Can users shift between constrained and exploratory modes?

  Allow users to move fluidly between direct commands and open-ended prompting when appropriate.

• Does the system set expectations for what kind of input/output is supported?

  Use affordances, hints, or examples to convey whether the AI supports simple commands, nuanced queries, or creative input.

Feedback & Adaptation

• Can users refine AI outputs over multiple iterations?

  Enable iterative refinement of AI-generated content.

• Are confidence levels communicated for important AI responses?

  Display confidence indicators to support informed decisions.

• Does the AI learn from user preferences and behavior?

  Adapt to user habits through preference learning.

Human in the Loop

• Are human approvals required for high-impact actions?

  Require critical review before executing significant actions.

• Can humans intervene during live AI operations?

  Allow real-time intervention to pause or adjust AI workflows.

• Does the system capture user feedback to improve AI over time?

  Gather continuous feedback to refine future system behavior.

• Does the design build user trust in the AI?

  Foster trust through explanations, transparency, and gradual exposure.

Workflow Visibility

• Is the AI's progress and state clear at a glance?

  Use clear progress indicators like status cards and progress bars.

• Are the stages of the AI's process transparent to the user?

  Make agent lifecycles (pending, active, complete) visible.

• Are updates consistent across all channels where users engage?

  Ensure cross-channel sync for a seamless user experience.