Short answer
Prompt engineering is the practice of designing instructions and context so an AI model gives useful, reliable and consistent output. In practice that means stating the goal, giving relevant background, assigning a role, specifying the format, adding examples, and asking the model to check its work. Modern practice also includes context engineering: supplying the right documents, data and tools.
By Chandan Maheshwari, AI mentor & consultant, founder of School of AI · Last updated
Key takeaways
- Context matters more than clever phrasing.
- Specify the output format you need.
- Examples improve consistency.
- Save prompts that work as reusable templates.
A simple prompt structure
A reliable pattern for everyday work.
- Goal — what you want and why
- Context — the facts, audience and constraints
- Role — who the AI should act as
- Format — table, bullets, email, word limit
- Examples — one good sample of the output
- Check — ask it to list assumptions or uncertainties
Common mistakes
One-line prompts with no context, asking for many things at once, accepting the first answer, and not saving prompts that worked.
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