Brex's Prompt Engineering Guide
翻译时间：2023年5月15日，注：原文后续会有更新
This guide was created by Brex for internal purposes. It's based on lessons learned from researching and creating Large Language Model (LLM) prompts for production use cases. It covers the history around LLMs as well as strategies, guidelines, and safety recommendations for working with and building programmatic systems on top of large language models, like OpenAI's GPT-4.
这份指南是由Brex为内部使用而创建的。它基于对研究和创建大型语言模型（LLM）用于生产环境的提示的经验教训。指南涵盖了LLM的历史，以及在使用和构建基于大型语言模型的编程系统时的策略、指南和安全建议，例如OpenAI的GPT-4。
The examples in this document were generated with a non-deterministic language model and the same examples may give you different results.
本文档中的示例是由非确定性语言模型生成的，同样的示例可能会给您带来不同的结果。
This is a living document. The state-of-the-art best practices and strategies around LLMs are evolving rapidly every day. Discussion and suggestions for improvements are encouraged.
这是一份持续更新的文档。关于LLM的最佳实践和策略正在每天迅速发展。欢迎进行讨论并提出改进的建议。
What is a Large Language Model (LLM)?
A Large Language Model is a prediction engine that takes a sequence of words and tries to predict the most likely sequence to come after that sequence1. It does this by assigning a probability to likely next sequences and then samples from those to choose one2. The process repeats until some stopping criteria is met.
一个大型语言模型是一个预测引擎，它接收一个词序列，并尝试预测在该序列之后最可能出现的序列1。它通过为可能的下一个序列分配概率，然后从中进行采样选择一个序列来实现这一目标2。该过程会重复进行，直到达到某个停止条件。
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