Interaction Design Policies

Guardrails in AI design are systems that ensure AI tools operate in alignment with an organization's standards, policies, and values. According to McKinsey, guardrails fall into five main types based on specific risks they address:

• Appropriateness guardrails filter out toxic, harmful, biased, or stereotypical content before it reaches users.
• Hallucination guardrails ensure AI-generated content doesn't contain factually wrong or misleading information.
• Regulatory-compliance guardrails validate that content meets general and industry-specific requirements.
• Alignment guardrails ensure that generated content aligns with user expectations and maintains brand consistency.
• Validation guardrails check that content meets specific criteria and can funnel flagged content into correction loops.^[1]

The appropriate guardrail implementation depends on context and industry. Financial or healthcare applications typically require stricter guardrails due to regulatory requirements and risk factors, while creative tools might allow more flexibility to support user expression. Effective guardrail design balances AI flexibility with the importance of safe, predictable outputs.

Content boundaries define what subject matter, ideas, and language an AI system is permitted to generate or respond to. Appropriateness guardrails implement these boundaries by checking if AI-generated content is toxic, harmful, biased, or based on stereotypes, filtering out any such inappropriate content before it reaches customers. These guardrails serve multiple purposes:

• Protecting users from harmful content
• Maintaining brand consistency
• Meeting regulatory requirements
• Ensuring appropriate experiences for different audiences

When establishing content boundaries, consider both prohibited content, or what the AI should never discuss, and encouraged content, or areas where the AI should excel. Boundaries are implemented through technical measures like prompt engineering, input filtering, output scanning, and human review processes. According to industry experts, boundaries should be regularly reviewed and updated as usage patterns evolve, new edge cases emerge, and societal standards change. Effective content boundaries are transparent to users, helping set expectations about what can be requested while maintaining AI usefulness.

Safety filters are the technical mechanisms that implement guardrails through active monitoring systems. Unlike content boundaries that define what's acceptable, safety filters are the operational components that analyze and intervene in real-time. Effective safety filter implementation requires a multi-stage approach: pre-processing filters that screen user inputs before they reach the AI model, runtime monitoring that observes generation patterns as they develop, and post-processing verification that examines completed outputs before delivery. These stages work together as a defense-in-depth strategy.

Safety filters use various technical approaches, including keyword matching, machine learning classifiers, semantic analysis, and pattern recognition.

The technical implementation should be calibrated based on risk levels with stricter settings for public-facing applications or services with vulnerable users. A critical but often overlooked aspect is the user experience when content is filtered, providing clear explanations about why something was blocked and offering constructive alternatives creates transparency without enabling circumvention.

Alignment guardrails ensure that generated content aligns with user expectations and doesn't drift away from its main purpose. These guardrails are essential for maintaining brand consistency in AI-powered experiences. When AI systems generate text, images, or other content, they must maintain the distinctive voice that users associate with the brand. This requires translating abstract brand values and personality traits into concrete parameters that guide AI outputs.

For example, a brand focused on accessibility might prioritize clear, jargon-free language in every interaction. Creating brand-aligned AI involves cataloging examples of brand communication, establishing tone guidelines across different contexts, and defining how brand identity manifests across various interaction types. These guidelines become part of the AI's training or operating parameters. Regular evaluation ensures the system continues to reflect the brand accurately, especially as the brand evolves over time or expands into new contexts.

Alignment guardrails ensure generated content matches user expectations, including appropriate tone and style. These parameters function as control mechanisms that influence the character of AI-generated content while maintaining underlying functionality. Well-designed AI systems offer appropriate tone variations based on context, user preferences, and task requirements. For instance, the same AI might respond differently when helping with professional documentation versus casual brainstorming.

Implementable tone parameters include:

• Formality level
• Technical complexity
• Sentence structure variety
• Use of figurative language
• Emotional resonance

Style controls can modulate conciseness, detail level, use of industry terminology, and examples selected. Advanced systems might adapt tone dynamically based on conversation history or user behavior, gradually matching the user's communication style.

Hallucination guardrails ensure AI-generated content doesn't contain information that is factually wrong or misleading. These guardrails help systems determine when to make definitive statements versus when to show uncertainty or cite sources. When implementing these guardrails, designers define confidence levels that shape how the AI responds based on how certain it is about information. For high-stakes topics like health or finance, stricter controls enforce source citations and clear uncertainty markers. Effective factuality design creates different types of responses, ranging from verified facts with citations to clear acknowledgments when the system is speculating.

Verification can include fact-checking against knowledge bases, requiring sources for claims, confidence scoring, and flagging statements that need verification. Well-designed systems make their confidence visible to users through both words and visual cues, helping people know how much to trust the information.

Hallucination guardrails need citation systems to show where information comes from. Good citation helps users trust AI by letting them check facts themselves. When designing citation features, it's important to balance being thorough with being user-friendly. Too many references overwhelm users, while too few leave them questioning reliability. Designers must decide the following:

• When citations are needed, usually for specific facts, quotes, or claims.
• What format to use, for example, formal citations or simple links
• How to display them: inline, footnotes, or expandable notes

The best citation systems also give context about how reliable and recent the sources are. For AI systems, it's especially important to clearly show the difference between information from verified sources and content the AI has generated on its own. This helps users know which parts they can fully trust and which might need verification.

Creating effective hallucination detection requires systematic testing approaches beyond basic accuracy measures:

• Red teaming: Specialized teams deliberately try to provoke hallucinations to uncover edge cases.
• Adversarial testing: Testing with questions in areas of limited AI knowledge to trigger false information, such as questions about obscure topics.
• Benchmark testing: Measuring performance against curated sets of factual and counterfactual statements.

When evaluating these systems, track both false positive rates (legitimate content incorrectly flagged) and false negative rates (hallucinations missed). Different applications require different priorities. Healthcare might minimize false negatives at the cost of more false positives, while creative applications might accept more false negatives to maintain fluid experiences. Regular human evaluation remains essential, as AI-based hallucination detectors themselves can fail in unexpected ways.

Validation guardrails check if AI content meets requirements, but what happens when content fails these checks? Recovery paths are the helpful responses an AI system provides when it can't deliver exactly what was requested. Good recovery paths transform potential disappointments into constructive experiences. Here are practical approaches for designing them:

• Clear explanations: Tell users why their request couldn't be fulfilled exactly as asked.
• Alternative suggestions: Offer similar but acceptable options instead of just saying "no."
• Refined prompts: Suggest better ways to phrase their request.
• Confidence options: Present multiple possibilities with indicators showing reliability.
• Human escalation: Offer connection to human assistance for complex issues.

Different issues need different recovery approaches. Content with potential factual errors might include sources with a disclaimer, while potentially harmful content might be met with alternative suggestions. Recovery paths work best when they're helpful rather than just restrictive. They should guide users toward successful outcomes even when the direct path isn't available.

Sometimes AI systems reach their limits. Human escalation is how we transition from AI to human assistance when needed. Clear triggers should initiate this process: user requests for human help, detection of sensitive topics, repeated user frustration, complex questions, or high-stakes decisions. Good escalation design feels smooth, not abrupt. Users shouldn't have to repeat information they already shared with the AI. The system should preserve conversation history and context during handoff.

When designing human escalation workflows:

• Make it clear when and why escalation is happening
• Set realistic expectations about response timing
• Keep users informed about where they are in the process
• Maintain a consistent tone between AI and human communication
• Use triage systems to prioritize urgent cases

Well-designed escalation presents human support as complementary to AI capabilities, not just a backup for failures. The best systems create a seamless experience where users feel their needs are being met, regardless of whether they're interacting with AI or humans.