Prompt engineering, primarily used in communication with a text-to-text model, is the process of structuring text that can be interpreted and understood by a generative AI model. Prompt engineering is enabled by in-context learning, defined as a model's ability to temporarily learn from prompts. The ability for in-context learning is an emergent ability of large language models.
A prompt is natural language text describing the task that an AI should perform. A prompt for a text-to-text model can be a query such as "what is Fermat's little theorem?" a command such as "write a poem about leaves falling", a short statement of feedback (for example, "too verbose", "too formal", "rephrase again", "omit this word") or a longer statement including context, instructions, and input data. Prompt engineering may involve phrasing a query, specifying a style, providing relevant context or assigning a role to the AI such as "Act as a native French speaker". Prompt engineering may consist of a single prompt that includes a few examples for a model to learn from, such as "maison -> house, chat -> cat, chien ->", an approach called few-shot learning.When communicating with a text-to-image or a text-to-audio model, a typical prompt is a description of a desired output such as "a high-quality photo of an astronaut riding a horse" or "Lo-fi slow BPM electro chill with organic samples". Prompting a text-to-image model may involve adding, removing, emphasizing and re-ordering words to achieve a desired subject, style, layout, lighting, and aesthetic.

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