Optimizing Compound AI Systems

This chapter explores the optimization and implementation of large language models (LLMs) and compound AI systems, focusing on frameworks like TensorRT. It discusses methodologies for combining models and data to enhance performance, highlighting the significance of structured task management, asynchronous processing, and the integration of human oversight. Key concepts such as function selection, task queues, and the development of guardrails to address AI weaknesses are also examined to improve overall system efficiency.