This chapter delves into the process of tracking accuracy, readability, and latency in AI models, emphasizing the use of a team of human contractors to rate completions for iterative model performance enhancement. It explores the concept of few-shot prompting for collecting data sets, training custom models, and enhancing model completions across various tasks. The discussion also covers the orchestration of asynchronous calls in AI tooling, trends in model usage for tasks like developing chatbots and analyzing operational data, and guidance on fine-tuning parameters in the RAG pipeline for creating AI systems.