What is Prompt engineering?
Prompt engineering is the practice of designing inputs to AI models so they reliably produce the output you want — structure, examples, roles, constraints and iteration.
Prompt engineering covers everything you can control on the input side of a language model: how you structure the instruction, what context and examples you supply, which role you assign, how you constrain and format the output, and how you break a hard task into steps. It's less about secret words and more about clear specification — the same discipline a good brief shows a human expert.
The name makes it sound technical; in practice it's a communication skill anyone can learn in days. The techniques with the strongest evidence are simple: give a role, give context, show examples, ask for step-by-step reasoning on hard problems, and have the model critique its own draft.
- Use a consistent structure (Role · Context · Task · Constraints · Output) so nothing gets forgotten.
- Change one thing at a time when a prompt underperforms — you learn what mattered.
- Keep the prompts that work in a library with a note on why they work.
Use it right now
Ask our brain anything on the homepage — it remembers the whole conversation — or write a brief in the Studio and see the prompt it compiles to.