User Feedback → AI Model Fine-tuning → Performance Monitoring

Create forms asking users to rate and compare AI responses (A/B format). Include questions like 'Which response was more helpful?' with simple rating scales and comment boxes for detailed feedback.

Set up automation to trigger when new Typeform responses arrive. Parse the preference data, categorize feedback types, and route high-priority issues to immediate review while batching routine feedback for model training.

Use the collected preference pairs to create training datasets. Implement reinforcement learning from human feedback (RLHF) by feeding preferred responses as positive examples and rejected responses as negative examples to improve model alignment.

Monitor key performance indicators like user satisfaction scores, response quality ratings, and engagement metrics. Set up dashboards to visualize how preference-based training affects real-world performance over time.