The Emerging Toolkit for Reliable, High-quality LLM Applications

Explore emerging techniques in "LLMOps" for building, tuning, and maintaining high-quality LLM-based applications in this 31-minute conference talk by Matei Zaharia. Discover tools for testing and visualizing LLM results, including those integrated into MLOps frameworks like MLflow. Learn about advanced programming frameworks such as Demonstrate-Search-Predict (DSP) that automatically improve LLM applications based on feedback. Gain insights into controlling LLM outputs and generating better training and evaluation data. Benefit from Zaharia's experience deploying LLMs in various applications at Databricks, including QA bots and code assistants. Understand how these techniques are being incorporated into MLOps products and MLflow to enhance the reliability and quality of LLM applications in high-stakes environments.