Prompt engineering is a technique for getting better results from large language models, such as GPT-4, by providing clear and specific instructions, examples and reference texts. Prompt engineering can help reduce the chances of producing undesired or inaccurate content, such as hallucinations, biases or offensive outputs. Prompt engineering can also help improve the model's reasoning and understanding of complex tasks.

OpenAI recently published a guide on how to use prompt engineering with the OpenAI API. The guide covers six strategies for getting better results: write clear instructions, provide reference text, split complex tasks into simpler subtasks, give the model time to "think", use inner monologue or a sequence of queries to hide the model's reasoning process, and use intent classification to identify the most relevant instructions for a user query. The guide also provides some examples of prompts that showcase what GPT models can do.

Prompt engineering is an important skill for anyone who wants to leverage the power of large language models for various applications. By following the best practices and tips from the website, users can improve their prompt design and get more reliable and useful outputs from GPT models.

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