Prompt engineering is the practice of crafting and refining the input instructions or queries given to generative AI models (such as large language models) in order to elicit the desired outputs​.Because models like GPT-3 or GPT-4 respond to whatever prompt they are given, how that prompt is formulated – the wording, context provided, format, etc. – can have a big impact on the quality and relevance of the model’s response. Prompt engineering often involves techniques like: providing clear instructions, giving the model a role or persona, supplying examples of the desired output format (few-shot prompting), or splitting complex tasks into multiple prompts (as in prompt chaining). The aim is to guide the model’s behavior without additional training, simply by exploiting the knowledge already present in the model and steering it with well-designed prompts​.For example, if one wants a model to generate an email, a prompt engineer might write: “You are a helpful assistant. Write a polite email to a coworker named Alex asking for an update on project X.” – this gives context and specifics that lead to a better result. Prompt engineering has become important as a way to get optimal performance from AI systems, especially when direct fine-tuning or retraining is not feasible. It requires understanding both the capabilities and limitations of the model, and iteratively adjusting the phrasing or structure of prompts to reduce ambiguity and bias in responses​.In essence, prompt engineering is about speaking the model’s language: finding the right input that makes the black-box model produce useful, accurate, and relevant output.