Is your RAG application confidently citing non-existent sources or inventing company policy?

Hallucinations often render AI products undeployable, damaging user trust and making compliance a nightmare.This workshop teaches you how to systematically detect, analyze, and mitigate hallucinations to build high-trust, production-ready AI. We move beyond theory to practical root cause analysis, examining why failures occur in Agents and RAG systems and how to fix them through data preparation, pipeline architecture, and context engineering. Bring your own project or use our templates to apply every technique in real-time.

Exclusive Masterclass Take-Homes

Cheat sheet: How to fix your LLM application

Key Learnings

Different hallucination types and their technical causes in RAG and agent systems, systematic observation to track when and how often hallucinations occur, data preparation techniques that reduce hallucination risk, pipeline architectures optimized for reliability, context engineering to improve grounding, selecting and configuring LLMs for specific use cases, building monitoring dashboards to reveal hallucination patterns, conducting root cause analysis to diagnose specific failures, controlled experiments to test mitigation strategies, continuous improvement workflows for production AI systems