This whitepaper provides a comprehensive overview of prompt engineering, explaining how to design effective inputs for large language models (LLMs) to guide their output. It covers various prompting techniques, including simple zero-shot and few-shot methods, along with more advanced strategies like Chain of Thought (CoT) and ReAct that incorporate reasoning and external tools. The document also discusses important LLM output configurations like temperature and sampling controls, and offers best practices for prompt creation, emphasizing clarity, specificity, and the importance of experimentation and documentation. Code prompting examples are included, demonstrating how LLMs can assist with generating, explaining, translating, and debugging code.

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