In this tutorial, we introduce Trace, a groundbreaking AutoDiff-like framework designed to train AI workflows end-to-end with rich feedback. Trace leverages numerical rewards, losses, natural language text, compiler errors, and more to achieve autonomous interactive optimization.

Key Takeaways:

Understanding the concept and vision behind Trace.

Exploring how Trace generalizes back-propagation for AI workflows.

Learning about how to use Trace to train Python workflows.

Discovering practical applications of autonomous interactive optimization (such as training LLM multi-agent systems, learning robot control policies, and autonomous prompt optimization).