A concrete example
Instead of saying "write a summary", you specify the audience, the length, the format, and the points not to miss.
Why it matters
Even with better models, the quality of your request still strongly shapes the result.
You practice it in ChatGPT, Claude, Gemini, Codex, n8n, or any tool that asks for an instruction.
Don't mix it up with
System prompt: The system prompt is the top-level instruction that frames how the model behaves during the conversation.
Few-shot / Zero-shot: Zero-shot means asking with no example. Few-shot means giving a few examples before your request.
Common mistakes
- Hunting for a universal formula.
- Giving too much useless context.
- Forgetting to show an example of the result you expect.
Quick checklist
- First I check whether the word names a concept, a tool, a risk, or a metric.
- I tie it to a concrete case: Instead of saying "write a summary", you specify the audience, the length, the format, and the points not to miss.
- I keep the main trap in mind: Hunting for a universal formula.
Quick questions
What is Prompt engineering in AI?
Prompt engineering means phrasing a request with enough context, examples, and constraints to get a useful answer.
Where will I run into Prompt engineering?
You practice it in ChatGPT, Claude, Gemini, Codex, n8n, or any tool that asks for an instruction.
Which word should I read next?
Start with System prompt, Few-shot / Zero-shot, Structured output.