Prompt Engineering Author: Lee Boonstra Prompt Engineering Acknowledgements Content contributors Michael Sherman Yuan Cao Erick Armbrust Anant Nawalgaria Antonio Gulli Simone Cammel Curators and Editors Antonio Gulli Anant Nawalgaria Grace Mollison Technical Writer Joey Haymaker Designer Michael Lanning February 2025 2 Table of contents Introduction 6 Prompt engineering 7 LLM output configuration 8 Output length 8 Sampling controls 9 Temperature 9 Top-K and top-P 10 Putting it all together 11 Prompting techniques 13 General prompting / zero shot 13 One-shot & few-shot 15 System, contextual and role prompting 18 System prompting 19 Role prompting 21 Contextual prompting 23 Step-back prompting 25 Chain of Thought (CoT) 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 translating code 46 Prompts for debugging and reviewing code 48 What about multimodal prompting? 54 Best Practices 54 Provide examples 54 Design with simplicity 55 Be specific about the output 56 Use Instructions over Constraints 56 Control the max token length 58 Use variables in prompts 58 Experiment with input formats and writing styles 59 For few-shot prompting with classification tasks, mix up the classes 59 Adapt to model updates 60 Experiment with output formats 60 JSON Repair 61 Working with Schemas 62 Experiment together with other prompt engineers 63 CoT Best practices 64 Document the various prompt attempts 64 Summary 66 Endnotes 68 Prompt Engineering You don’t need to be a data scientist or a machine learning engineer – everyone can write a prompt. Introduction When thinking about a large language model input and output, a text prompt (sometimes accompanied by other modalities such as image prompts) is the input the