Guardrails, Monitoring, and Continuous Tuning for Safer Gen AI Apps

This demo walks through how to augment a banking chatbot with built-in guardrails, real-time model monitoring, and continuous fine-tuning using MLRun. You’ll learn how to:

• Log and evaluate every LLM interaction using LLM-as-a-judge
• Detect risks like hallucinations or off-topic responses with custom monitoring plugins
• Use monitored data to automatically fine-tune your LLMs and improve accuracy over time
• Perform rolling model upgrades without disrupting service
• Build and schedule a fully automated retraining pipeline to continuously enhance GenAI performance

The result? A safer, more robust gen AI application that gets smarter with every interaction – without manual intervention.