Prompt engineering is the practice of carefully crafting the instructions, questions, or context given to a language model in order to reliably get a specific, high-quality output from it. Since a model's response can shift significantly based on exactly how a request is phrased, what examples are included, and how the task is framed, prompt engineering treats the wording of a request as something worth deliberately designing rather than writing off the cuff.
Common prompt engineering techniques include providing examples of the desired output format (few-shot prompting), explicitly asking the model to reason step by step before answering, and clearly specifying constraints like tone, length, or audience. It's become an increasingly valuable skill as more people and businesses rely on large language models for real work, since a well-engineered prompt can often get dramatically better results out of the exact same underlying model than a vague or poorly structured one.
