Prompt engineering is the deliberate wording and structuring of prompts to more reliably get better, more precise results from an AI model.
Rather than just asking off the cuff, prompt engineering uses established techniques such as clear role assignment ("You are an experienced tax advisor"), concrete format instructions, few-shot examples, or explicitly requesting intermediate reasoning steps (chain-of-thought) to improve answer quality.
Good prompts spell out context, target audience, desired format and constraints as explicitly as possible, rather than assuming the model shares implicit knowledge.
While simple prompt optimization is enough for most everyday tasks, for very specific, recurring use cases, fine-tuning the model can be more effective than ever more elaborate prompts.
