Prompt Engineering Author: Lee Boonstra Prompt Engineering February 2025 2 Acknowledgements Content contributors Michael Sherman Yuan Cao Erick Armbrust Anant Nawalgaria Antonio Gulli Simone Cammel Grace Mollison Technical Writer Joey Haymaker Designer Michael Lanning Introduction 6 Prompt engineering 7 LLM output configuration 8 Output length 8 Sampling controls 9 Temperature 9 Top-K and top-P 29 Self-consistency 32 Tree of Thoughts (ToT) 36 ReAct (reason & act) 37 Automatic Prompt Engineering 40 Code prompting 42 Prompts for writing code 42 Prompts for explaining code 44 Prompts for

new category of LLM-powered systems known as agents. This guide is designed for product and engineering teams exploring how to build their first agents, distilling insights from numerous customer deployments agent’s abilities, and you can diagnose where smaller models succeed or fail. In summary, the principles for choosing a model are simple: 01 Set up evals to establish a performance baseline 02 Focus handoffs that transfer execution between agents. Regardless of the orchestration pattern, the same principles apply: keep components flexible, composable, and driven by clear, well-structured prompts. 17

requires careful engineering optimization to manage the GPU memory and RAM pressure, and meanwhile maintain a fast training speed. For this goal, we implement the following engineering optimizations. (1) serving with pagedattention. In Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles, 2023. G. Lai, Q. Xie, H. Liu, Y. Yang, and E. H. Hovy. RACE: large-scale reading comprehension preprint arXiv:2311.07911, 2023. 26 Appendix A. Contributions and Acknowledgments Research & Engineering Aixin Liu Bingxuan Wang Bo Liu Chenggang Zhao Chengqi Deng Chong Ruan Damai Dai Daya Guo Dejian

Stanford University… 1: AI ‘Winter’ was a term used by Nils J. Nilsson, the Kumagai Professor of Engineering in computer science at Stanford University, to describe the period during which AI continued to would understand goals, generate plans, and self-correct in real time. They could drive research, engineering, education, and logistics workflows with little to no human oversight – handling ambiguity and databases. Model development = frameworks for modeling & training, inference optimization, dataset engineering, & model evaluation. Application development = custom AI-powered applications (varied use cases)

Frameworks New operator introduced by operator fusion optimization potential benefit: 1.5x speedup Engineering intensiveMachine Learning based Program Optimizer TVM: Learning-based Learning System High-level

Developer resources are the main bottleneck and growth inhibitor in many organizations. 
 When engineering teams are overwhelmed, it slows innovation and creates an insurmountable backlog of apps and ideas