Prompt Engineering

The art of creating prompts that elicit the desired responses from a large language model. Humans perform prompt engineering. Writing well-structured prompts is an essential part of ensuring useful responses from a large language model. Prompt engineering depends on many factors, including: - The dataset used to pre-train and possibly fine-tune the large language model. - The temperature and other decoding parameters that the model uses to generate responses. Prompt design is a synonym for prompt engineering. See Introduction to prompt design for more details on writing helpful prompts.

Prompt engineering in generative AI is the process of crafting effective prompts to elicit desired outputs from large language models (LLMs). It's an iterative, test-driven process focused on refining inputs to achieve specific outcomes. This involves considering both the content and structure of the prompt to ensure accurate and high-quality responses. Effective prompt engineering is crucial for complex tasks, even though simpler tasks may not require it. The goal is to rapidly prototype LLM-based applications. For more information, see Introduction to prompt engineering.

Prompt engineering is the act of writing and re-writing prompts to produce outputs that align with your users' expectations. A well-written prompt states how the LLM should build its response, consists of multiple components that can be versioned, tested, and improved over time, and can act as a shared artifact for collaboration across teams.