User experience is the quality of interaction between people and an AI system, shaped by whether users can understand, check, and safely use its outputs. In enterprise GenAI applications, UX connects the AI stack to user needs and supports responsible adoption. section 13.3.3 section 10.2.1
A practical UX design places users inside the safety loop rather than treating prompts or model behavior as the only control. It works alongside response filtering, secure integrations, orchestration, monitoring, documentation, and feedback-driven iteration. section 13.3.3 section 10.2.1
Useful user-centric interventions include:
- Review and edit generated content before it is used, correcting inaccuracies and preserving user accountability. section 13.3.3
- Cite reference-based AI content so users can inspect its origin. section 13.3.3
- Disclose when users are interacting with AI or viewing AI-generated content. section 13.3.3
- Use predetermined responses and input/output limits for potentially harmful or misusable requests. section 13.3.3
- Place guidance about prompting, checking outputs, and system limitations directly in the interface when feasible. section 13.3.3
Review-before-use workflow
A responsible AI assistant can show generated content with citations, identify it as AI-generated, and provide an explicit review-and-edit step before the user adopts it. Evaluation then checks whether the resulting interaction matches user needs and expectations. section 13.3.3 section 12.1
Table 13.2 Application-level RAI mitigations
|
Mitigation
|
Description
|
|---|---|
|
Review and edit
|
Encourage users to critically assess AI-generated outputs, supporting efficient correction and highlighting potential inaccuracies.
|
|
User responsibility
|
Remind users of their accountability for the final content, especially when reviewing suggestions such as code.
|
|
Citations
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If AI content is reference based, cite sources to clarify the origin of the information.
|
|
Predetermined responses
|
For potentially harmful queries, provide thoughtful, precrafted responses to maintain decorum and direct users to appropriate policies.
|
|
Input/output limitation
|
Restrict input and output lengths to minimize the production of undesirable content and prevent misuse.
|
|
AI role disclosure
|
Inform users they are interacting with an AI, not a human, and disclose if the content is AI generated, which may be legally required.
|
|
Bot detection
|
Implement mechanisms to prevent the creation of APIs over your product, ensuring controlled use.
|
|
Anthropomorphism prevention
|
Anthropomorphism prevention means ensuring that AI systems don’t seem human. It’s about clear communication that AI doesn’t think or feel to avoid confusion and ensure people use AI properly, without expecting it to act like a human. Implement safeguards against AI outputs that suggest human-like qualities or capabilities, reducing misinterpretation risks.
|
|
Structured inputs/outputs
|
Use prompt engineering to structure inputs and limit outputs to specific formats, avoiding open-ended responses.
|
Application-level mitigation patterns provide a compact checklist for designing safer GenAI interactions. section 13.3.3
