A Practitioner's Guide to Safeguarding LLM Applications

Learn essential techniques for enhancing the reliability and security of Large Language Model (LLM) applications in this comprehensive workshop presented by Dice Health Co-Founder Shashank Shekhar at the Toronto Machine Learning Series. Discover practical solutions to common LLM challenges including inconsistent outputs, off-topic responses, and sensitive data exposure. Master methods for generating structured outputs, ensuring topic relevance, mitigating hallucinations, and protecting company data through hands-on programming exercises using open-source tools. Designed for data scientists, ML engineers, and professionals involved in real-world LLM application development, gain a thorough understanding of current LLM limitations and acquire practical tools to build more robust and secure AI systems.