AI Governance Reference for Prompt Engineers: Building AI Agents

As AI Agents become increasingly integrated into business operations, ensuring their responsible and ethical use is paramount. This is where AI governance comes in. Think of AI governance as a set of guidelines and best practices that ensure AI agent development and utilization align with our values and help avoid potential harms.

This comprehensive guide explains the core principles of AI governance and how they directly apply to your crucial role as a prompt engineer.

AI Governance for Prompt Engineers: Your Role & Responsibilities

As a prompt engineer, you are an “AI whisperer.” You bridge human intention and machine capabilities through the language of prompts. This unique role means you have a significant responsibility in shaping how AI agents behave and impact the world.

1. AI Governance Structures: Setting Ground Rules for Ethical Prompt Engineering

AI governance structures are the “ground rules” for responsible AI development within your organization. These structures ensure that AI initiatives, including your work as a prompt engineer, align with ethical principles and avoid potential harms.

Key Components of AI Governance Structures:

• AI Principles: High-level ethical guidelines that dictate how AI is developed and used (e.g., fairness, transparency, accountability).
• AI Policies: Specific rules derived from AI principles, addressing areas like data privacy, bias mitigation, and risk management.
• AI Ethics Boards: Committees that provide oversight, review AI projects, and handle ethical concerns.

Your Role as a Prompt Engineer:

• Understand the Rules: Familiarize yourself with your organization’s AI principles, policies, and relevant regulations.
• Collaborate: AI governance is a team effort. Work with data scientists, legal experts, and other stakeholders to ensure alignment with organizational values.

2. Data Governance: Building Trust Through Data Integrity

Data is the fuel of AI. If the data used to train your AI agent is biased, incomplete, or unreliable, the agent’s outputs will reflect those flaws.

Key Considerations for Data Governance:

• Data Quality: Is the data accurate, complete, and relevant to the task the AI is being trained for?
• Data Bias: Does the data unfairly represent or disadvantage certain groups of people?
• Data Privacy: Does the data contain sensitive personal information that needs to be protected?

Your Role as a Prompt Engineer:

• Data Awareness: Understand the origin of the training data. Ask questions about its collection, labeling, and potential biases.
• Bias Mitigation: Actively consider and mitigate potential biases in your prompts. This might involve rephrasing prompts, adjusting data weighting, or using fairness-aware algorithms.
• Data Protection: Never include sensitive personal information in prompts unless absolutely necessary and only with proper authorization and security measures in place.

3. Model Governance: Documenting Your Agent’s Journey

Model governance focuses on the responsible development, deployment, and management of AI models. This ensures they are well-documented, auditable, and aligned with ethical standards.

Your Role as a Prompt Engineer:

• Prompt Documentation: Maintain clear records of your prompt engineering process, including:
  • The purpose of the agent and intended tasks.
  • The thought process behind your prompt design.
  • Any assumptions made about the data or the AI’s capabilities.
  • Decisions made to address bias or fairness concerns.
  • Results of testing and evaluation.
• Version Control: Keep track of changes to your prompts over time using version control systems. This helps track the agent’s evolution and identify the source of any issues.

4. Monitoring & Issue Resolution: Maintaining Agent Integrity

Even well-designed AI agents can behave in unexpected or undesirable ways. Continuous monitoring and a clear process for addressing issues are essential.

Your Role as a Prompt Engineer:

• Regular Monitoring: Establish a system for regularly reviewing the outputs of your AI agents, using automated tests, human evaluation, or both.
• Performance Evaluation: Track key metrics related to your agent’s performance (accuracy, bias, user feedback) and look for patterns or anomalies.
• Issue Reporting & Resolution: Have a clear process for reporting and addressing any issues identified during monitoring. This might involve refining prompts, retraining the AI model with improved data, or taking the agent offline.

Key Takeaways for Prompt Engineers

• You are a steward of ethical AI: Your decisions directly impact the behavior and real-world consequences of AI agents.
• AI governance is an ongoing process: It requires continuous attention, collaboration, and a willingness to adapt as AI technology evolves.

By embracing these principles, you can help ensure that the AI agents you help create are used responsibly, ethically, and for the benefit of all. To delve deeper into specific AI governance frameworks and resources, consider exploring organizations like the Partnership on AI or the AI Now Institute.