"Prompt Engineering," authored by Lee Boonstra in September 2024, offers a comprehensive guide to crafting effective prompts for large language models. It begins by introducing the fundamentals of prompt engineering and the importance of model configuration. The paper then explores various prompting techniques, including zero-shot, few-shot, system, role, contextual, step-back, and chain-of-thought prompting, as well as more advanced methods like self-consistency, tree of thoughts, ReAct, and automatic prompt engineering. Furthermore, the document discusses code prompting for generation, explanation, translation, and debugging. Finally, it concludes with best practices for designing, testing, and documenting prompts to optimize LLM outputs.

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