A good prompt defines task, context, limits, and review format

Short requests like “make a plan” leave important decisions to the model. A helpful instruction describes the authorized work and material, shows what should not be invented, and specifies how to raise questions.

Use a simple structure

Include: task (what to do), context (what it will be used for), sources (what you can consider), limits (what not to conclude or reveal), format (how to organize the answer) and checking (what to flag for human review).

Example: “Summarize the notes below into up to five bullet points for a support team. Use only the notes; do not invent dates or causes. Separate facts from questions and mark ambiguous passages for review. Preserve any identifiable information? Do not: replace names with functions.” Adapt the last limit to your organization's data policy.

Iterate with examples and criteria

Test the prompt on common and difficult inputs. Compare the answer against defined criteria: completeness, clarity, fidelity to the source and handling of uncertainty. When it fails, adjust instructions or flow and save regression examples.

Avoid entering credentials or personal data without appropriate basis. A well-written prompt reduces ambiguity, but the output may still contain errors or invented information; validate important claims before use.

Frequently asked questions

Are long prompts always better?

No. Include context that changes the answer and remove repeated instructions. Test whether every detail improves real results.

Can I ask that AI never makes mistakes?

No. Ask them to indicate uncertainty and limits, and check relevant facts from an appropriate source.

Should I save my prompts?

Save useful versions along with input examples, criteria and review date, without including sensitive data.

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