User Agency & Customization

Different AI systems need different levels of user control. For recommendation systems, users rarely want to adjust the algorithm directly. Instead, they want ways to give feedback or fix mistakes. For creative AI tools like image or text generators, users often need more direct control to get exactly what they want. The right amount of control also depends on who is using the system and how important the task is. Beginners or people doing simple tasks usually prefer fewer controls with good default settings. Experts or people making important decisions often need more detailed controls.

For example, a doctor using an AI tool to help diagnose patients would want to see confidence levels and adjust settings. A person using an app to enhance photos might prefer simple preset options. When deciding where to add controls, look for situations where people's goals might vary significantly, where mistakes could have serious consequences, or where personal preference strongly affects whether the result is good. Remember that each control makes the interface more complex, so only add controls that truly help users.

Sliders are a good way to control continuous AI parameters across many different applications. In photo editing apps like Lensa AI, sliders control parameters such as "Face retouch," "Deep retouch," "Neck retouch," and "Eye bags" adjustments. In text generation, sliders might control verbosity or creativity. In voice AI, they might adjust the speaking rate or accent strength. Unlike on/off toggles or radio buttons, sliders allow for fine adjustments along a range.

When designing sliders for AI control, clearly label what the slider adjusts and what each end of the range means. For example, a face retouch slider might show a minimal value on the left and maximum on the right, with numbers (like 0-1) to indicate intensity. This helps users understand how moving the slider will affect the results. For less intuitive parameters, showing visual examples of how different slider positions change the output helps users make better choices.

Consider whether the slider should have specific steps or be completely smooth. Steps work well when there are meaningful thresholds or when users might want to reproduce exact settings later. Smooth sliders offer more precise control but make exact reproduction harder. Also, consider whether to show numerical values next to sliders, as seen in the Lensa AI example, where "1" indicates the current intensity level. These numbers help with precision but add visual complexity.

Many AI applications offer preset modes that package multiple parameter settings into single, easy-to-understand options. For instance, Bing AI provides "More Creative," "More Balanced," and "More Precise" conversation styles that adjust how the AI generates responses. These modes change multiple underlying parameters simultaneously to create distinctly different interaction styles without requiring users to understand the technical details. The key benefit of preset modes is simplification. Rather than requiring users to understand and adjust multiple technical parameters individually, users can select a mode that matches their goal. This prevents users from creating inconsistent parameter combinations while still offering meaningful control over the AI's behavior.

Effective preset modes use clear, descriptive labels that communicate the practical difference between options. In Bing's case, users understand intuitively that "More Creative" might produce more varied, imaginative responses, while "More Precise" will focus on accuracy and factuality. The number of modes should remain small, typically 3-5 options, to avoid overwhelming users with choices. When implementing preset modes, consider providing a brief explanation or example of what each mode does best.

Beyond factory presets, advanced AI interfaces often allow users to save their own custom parameter combinations as personal presets. This approach is common in professional AI tools where users develop specific preferences over time. For example, Midjourney lets users create custom parameter combinations and save them for future use. Professional audio AI tools allow saving processing chains as presets for consistent results across projects. These personalized settings profiles help users maintain consistency across projects and save time when working on similar tasks. When designing systems for user-defined presets, include clear naming capabilities, preview functionality, and organization options for managing multiple profiles. Consider allowing users to share presets with teammates or communities, as many creative AI tools now support. This collaborative aspect creates efficiency and consistency for teams while fostering a community around the tool.

Progressive disclosure in AI interfaces strategically reveals controls based on user expertise and needs. This approach starts with a simplified interface showing only essential controls, then provides paths to more advanced options as users need them. For example, ChatGPT initially presents just a basic prompt field, but uses contextual tooltips to introduce new features like "Connect Apps" only when relevant, rather than overwhelming users with all capabilities at once. This pattern respects both beginners and experts.

Novices aren't overwhelmed by complexity they don't yet need, while advanced users can access powerful features when necessary. Common implementations include tooltips highlighting new features, expandable panels, and "Settings" buttons that hide less frequently used options. When implementing progressive disclosure, ensure that default settings work well for most users most of the time, as many will never explore advanced options. Make paths to advanced controls easy to discover without being distracting. Consider using a staged introduction approach where new capabilities are introduced through tooltips or highlighting only after users have mastered basic interactions. This creates a natural learning curve that builds user confidence while gradually revealing the system's full potential.

Default settings in AI interfaces are what users see first when they start using the system. Good defaults work well for most people without any changes needed. They represent the designer's best guess of what will satisfy most users in common situations. Creating good defaults requires learning about typical users through research. While it might seem ideal to personalize everything for each user, starting with settings that work well for most people reduces mental effort. Look at which settings produce good results for the largest number of users. The few users with special needs can still adjust settings if needed. Defaults should also consider safety and ethics. In generative AI, default settings usually prioritize safe, reliable outputs over maximum creativity. Privacy settings often start with more protection, even if some users might prefer to share more data for better features. Remember that defaults influence user behavior. Users often keep the default settings even if they aren't perfect because changing settings takes extra effort.

Adding customization options makes interfaces more complex and costs development time. They must prove their value through measurable improvement in user experiences. A good evaluation combines quantitative metrics with qualitative insights to determine whether customization features actually benefit users. Key quantitative metrics include:

• Adoption rate: What percentage of users adjust default settings?
• Retention impact: Do users who customize stay longer?
• Outcome improvement: Do customized settings produce measurably better results?

Low adoption might indicate either unnecessary controls or poor discoverability. Look at these metrics across different user groups, as expert users often show different patterns than beginners. Qualitative evaluation provides crucial context through user interviews, satisfaction surveys, and usability testing. Watch users interact with customization features. Do they understand what controls do? Can they achieve desired outcomes? Do they express frustration or confidence? Pay particular attention to whether users' understanding of how parameters work matches how they actually function. The most valuable measurement approaches combine behavior data with direct user feedback. For example, if logs show users frequently adjusting a parameter but then going back to the default, follow-up research might reveal confusion about its function or dissatisfaction with the resulting changes. These are insights that wouldn't be clear from the numbers alone.

AI assistants increasingly allow users to set specific instructions for how the system should respond. ChatGPT, Claude, and other conversational AI systems offer interfaces where users can write detailed guidelines about tone, formatting, terminology, and other response characteristics. This approach gives users direct control over AI behavior through natural language rather than technical parameters.

Some interfaces combine free-form instructions with pre-defined trait tags like "Witty," "Concise," or "Detailed" that users can select to quickly adjust the AI's personality. The power of instruction-based customization lies in its flexibility and precision. Users can specify exactly what they need without understanding the technical implementation. For professional users, this feature allows adapting the AI to specific domains by requesting domain-specific terminology or formatting conventions. For personal use, it helps create more satisfying interactions by aligning the AI's communication style with user preferences.