RAISP02-BP06 Enable users to customize core AI system behaviors

When adding guardrails to your system, build adjustable controls that let users determine how strictly content gets filtered. Set up multi-level filtering from low to high for different content types and create user roles where administrators set baseline policies while end users can adjust settings within safe limits. Include feedback systems to track how well these guardrail controls work and improve them over time.

Design interfaces for adjusting output content, style and tone. Allow users to adjust inference parameters like Temperature, Top P, and Top K for text generation to balance between creative and focused outputs. These parameters control the output by influencing the token selection process. Temperature determines randomness of token selection, with higher values producing more creative text and lower values resulting in more focused output. Top P (Nucleus Sampling) samples tokens whose cumulative probability sums to a given threshold, dynamically adjusting the option pool. Top K restricts the model's choice to a fixed number of highest-probability tokens. Similarly, provide ways to the user to adjust response length, format options, and output style.

Design structured prompting frameworks to enhance user control over AI system behavior and outputs. Create system-level prompt templates that allow administrators to define AI personality, tone, and response boundaries, while enabling end users to customize task-specific instructions within safe limits. Build prompt libraries with preset configurations for common use cases (for example, professional communication, creative writing, technical analysis) that users can select and modify. Include prompt validation mechanisms to assist user-provided prompts align with safety guidelines while maintaining the desired level of control over AI responses. Design prompt management interfaces that assist users to understand prompt effectiveness through clear feedback and iterative refinement options.

Design tracking mechanisms for control adjustments, system responses, user interactions and performance impacts.

Create feedback mechanisms that enable users to refine AI behavior over time. This assists to maintain relevance and reliability of the AI system based on user input and preferences.

Develop role-based customization options, allowing different levels of AI feature access and customization based on user roles and business requirements.