This whitepaper introduces prompt engineering, defining it as the process of crafting effective inputs to guide large language models (LLMs) in generating accurate outputs. It explores various prompting techniques, such as zero-shot, one-shot, and few-shot prompting, which involve providing no, one, or multiple examples to the model. The document also distinguishes between system, contextual, and role prompting, highlighting how each sets the model's overall purpose, provides task-specific details, or assigns a specific persona. Furthermore, it covers advanced methods like step-back prompting for abstract reasoning, Chain of Thought (CoT) for breaking down complex problems, self-consistency for improved accuracy through diverse reasoning paths, Tree of Thoughts (ToT) for exploring multiple reasoning paths simultaneously, and ReActfor enabling LLMs to interact with external tools. Finally, the paper offers practical best practicesfor prompt engineering, including designing with simplicity, being specific about desired outputs, using instructions over constraints, and documenting prompt attempts. Hosted on Acast. See acast.com/privacy for more information.

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