What is prompt engineering?
Prompt engineering is the practice of shaping an AI model’s behavior using carefully written instructions, context, and constraints. It blends UX writing, system design, and QA: you set the role, provide relevant data, define the goal and output style, and then iterate based on feedback. Good prompting reduces ambiguity, encodes business rules, and makes results repeatable. It isn’t about trick phrases; it’s about clarity, structure, and evaluation. As models evolve, the core stays the same: specify intent, supply context, and verify the output against clear criteria.
In practice
Think in inputs and invariants: what the model must always know, what it must never do, and how success will be judged. Put those in writing inside the prompt.
Where to start
- [01]List your most common AI tasks and their success criteria.
- [02]Draft role + objective + constraints templates for each task.
- [03]Collect 10 real examples per task to use as calibration data.
- [04]Establish a quick review checklist before deploying any prompt.
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