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Podcast Description: This whitepaper on prompt engineering provides a comprehensive overview of how to effectively interact with large language models (LLMs). It introduces the concept of prompt engineering as the process of crafting inputs to guide LLMs towards desired outputs, emphasizing that this is an iterative process accessible to everyone. The document explains crucial LLM output configurations, such as token limit and sampling controls (temperature, top-K, top-P), which influence the nature of generated text. A significant portion focuses on various prompting techniques including zero-shot, few-shot, system, contextual, and role prompting, illustrating how different approaches can shape the model's response. More advanced techniques like step-back prompting, Chain of Thought, self-consistency, Tree of Thoughts, and ReAct are also detailed, showcasing methods for enhancing reasoning and incorporating external tools. Finally, the paper offers valuable best practices for prompt design, covering clarity, specificity, using instructions over constraints, controlling output length, using variables, experimenting with formats, and the importance of documenting prompt attempts.