Prompt engineering is a relatively new discipline focused on developing and optimizing prompts to efficiently use language models (LMs) for various applications. It involves structuring natural language inputs, known as prompts, to produce specific outputs from generative AI models. The skills associated with prompt engineering help in understanding the capabilities and limitations of large language models (LLMs). Researchers use it to improve LLMs on tasks like question answering and reasoning, while developers design effective prompting techniques for interfacing with LLMs and other tools.
Prompt engineering encompasses a range of skills and techniques for interacting with and developing LLMs. It's considered an important skill for building with and understanding the capabilities of LLMs and can improve their safety and enable new capabilities, such as augmenting LLMs with domain knowledge and external tools. The process involves designing clear queries, refining wording, providing relevant context, specifying the style of output, and assigning a character for the AI to mimic to guide the model toward more accurate, useful, and consistent responses.
