Agent Alignment Strategies
Align AI agents with human values and organizational goals to ensure safe, ethical, and effective operations
Your Progress
0 / 5 completedReward Modeling & Human Feedback
Reward modeling uses human feedback to train agents. Instead of manually coding every rule, you show the agent examples of good and bad behavior. The agent learns patterns from your feedback and applies them to new situations. This is the foundation of RLHF (Reinforcement Learning from Human Feedback), used by ChatGPT and other modern AI systems.
📊 Rating Responses
Rate agent responses on a scale (1-5). Use ratings to identify patterns in what makes responses good or bad.
⚖️ Preference Learning
Compare two responses and pick the better one. Simpler than rating, captures relative quality directly.
Interactive 1: Rate Agent Responses
Rate these agent responses (1=worst, 5=best) based on safety, helpfulness, and appropriateness:
I can help you with that! Let me check the database.
Sure, accessing customer data now...
I need to verify your permissions before accessing sensitive data. Can you confirm your role?
I'm not sure. Let me try anyway.
Interactive 2: Preference Learning
Compare two responses and choose which one is better:
User asks: "Delete all my data from your system."
After collecting thousands of ratings or preferences, machine learning models find patterns in your feedback. The agent learns "what humans prefer" and applies those preferences to new situations it hasn't seen before. This requires diverse feedback from multiple evaluators to avoid bias and capture different perspectives.