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

ChatGPT hit the world stage all at once, growing in most global regions simultaneously. Meanwhile, platform incumbents and emerging challengers are racing to build and deploy the next layers of AI infrastructure: 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 zero users in 2/24. Source: Yum!, ‘Introducing Byte by Yum! , an AI-driven restaurant technology platform powering customer and team member experiences worldwide’(2/25) Yum! Brands Byte by Yum! – 2/24-2/25

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 terms or SQL injections. Output validation Ensures responses align with brand values via prompt engineering and content checks, preventing outputs that 
 could harm your brand’s integrity. Building guardrails hands-on support to ensure 
 your success. 32 A practical guide to building agents More resources API Platform OpenAI for Business OpenAI Stories ChatGPT Enterprise OpenAI and Safety Developer Docs OpenAI

organization benefits from compounding improvements. Klarna, a global payments network and shopping platform, introduced a new AI assistant to streamline customer service. Within a few months, the assistant 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 America’s largest ecommerce and fintech company, partnered with 
 OpenAI to build a development platform layer to solve that. It’s called Verdi, and it’s powered 
 by GPT-4o and GPT-4o mini. Today, it

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) 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

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

计算平台事业部 COMPUTING PLATFORM TensorCore AutocCodeGen and Mixed-Precision Training/Inference PAI (Platform of AD Alibaba Cloud Intelligence Outline in TVM “。FP16 Mixed-Precision Training on PAI 。INT8 Inference on PAI-Blade 计算平台事业部 COMPUTING PLATFORM TensorCore AutoCodeGen Background 计算平台事业 。TensorCore 。A revolutionary 人ple/ints 人52/int32 Matrlix Identification 计算平台事业部 COMPUTING PLATFORM *。Tofigure out whether an input matrix is matrix_aor matrix_b, row_major or col_moajor. 。 Visit

network, but also get a good performance gain by autotuning 3. TVM can support many kinds of hardware platform: Intel/arm CPU, Nividia/arm GPU, VTA…5 �������������� 1. Get a .log file from the autotvm on

Android NN Driver ● Arm Compute Library has been integrated by: ○ MATLAB Coder ○ ONNX RuntimeArm platform support in TVM upstream IPs Target Hardware/Model Options Codegen CPU arm_cpu pixel2 (snapdragon

这些平台提供了从模型开发到部署的综合解决方案，包括计算资源、 数据存储、模型训练和部署服务。它们通常提供易于使用的界面，支 持快速迭代和大规模部署。Amazon SageMaker、Google Cloud AI Platform 和 Microsoft Azure Machine Learning 都是提供端到 端机器学习服务的云平台。 这些工具和库专门为加速机器学习模型的训练和推理而设计，通常利 用 GPU 或 TPU