Google: The Art and Science of Prompt Engineering

This paper serves as an introduction to **prompt engineering**, the process of crafting high-quality text inputs to guide Large Language Models (LLMs) in producing accurate and desired outputs. It explains that anyone can write a prompt, but effective prompting requires understanding how LLMs predict the next sequence of words based on their training data and carefully designing prompts by considering factors like model choice, configurations, wording, style, structure, and context. The notebook delves into various **prompting techniques**, ranging from simple **zero-shot prompts** that rely solely on task descriptions to more complex methods like **few-shot prompting** that provide examples, **Chain of Thought (CoT)** prompting that encourages step-by-step reasoning, and **ReAct** prompting that combines reasoning with external tool interactions. Furthermore, it emphasizes the importance of understanding and configuring **LLM output settings** like temperature and sampling methods to control the randomness and creativity of the model's responses. Finally, the document outlines **best practices** for prompt design, including providing examples, maintaining simplicity, being specific about desired outputs, using instructions effectively, managing token length, utilizing variables, experimenting with formats, and documenting efforts for continuous improvement.

https://drive.google.com/file/d/1AbaBYbEa_EbPelsT40-vj64L-2IwUJHy/view