Prompts / Prompting & AI Workflows

ChatGPT prompt writing tips

This prompt produces practical prompt-writing guidance for a specific task, not a generic list of AI advice. Use it when an answer from ChatGPT, Claude, or Copilot is vague, unreliable, or poorly structured.

PromptOpen ChatGPTOpen Claude
Help me design a prompt that consistently gets useful results for this task: [TASK TO IMPROVE]. I will use [AI TOOL] and the output will be used by [INTENDED USER OR AUDIENCE]. Here is an example of a weak result or current prompt: [CURRENT PROMPT OR BAD OUTPUT]. My available source material and non-negotiable constraints are: [SOURCES AND CONSTRAINTS].

First diagnose the failure in the current prompt or output. Identify which of these are missing or underspecified: role or expertise, task boundary, source inputs, output structure, evaluation criteria, uncertainty handling, audience, and constraints. Do not claim a capability the tool may not have, such as live web access, file access, or guaranteed factual accuracy.

Then produce:
1. A short diagnosis with the three highest-impact changes.
2. A reusable prompt of 180-260 words, written in first person, with square-bracket placeholders for inputs I must supply.
3. A compact input checklist explaining what each placeholder needs.
4. Two realistic example prompts for the same task: one quick version and one high-stakes version.
5. A “quality gate” of 4-6 checks I can ask the AI to perform before it answers.
6. Three follow-up messages for improving an initial answer without restarting.

Make the prompt operational: specify a deliverable, audience, format, decision criteria, and how to label missing evidence. Avoid empty instructions such as “make it engaging” unless you turn them into observable standards. Before answering, check that every recommendation relates to my stated task and that the proposed prompt does not request invented facts, citations, calculations, or credentials. Ask up to 3 clarifying questions only if a required input is missing.

Fill in

PlaceholderWhat to enterExample
[TASK TO IMPROVE]Describe the work you want the AI to do in one specific sentence.Turn customer interview notes into prioritized product insights for a quarterly planning meeting.
[AI TOOL]Enter the tool you plan to use, such as ChatGPT, Claude, or Copilot.Copilot Chat
[INTENDED USER OR AUDIENCE]State who will use or read the output and what they need from it.A product manager presenting to engineering and customer success leads.
[CURRENT PROMPT OR BAD OUTPUT]Paste your current prompt or a short example of an unsatisfactory answer.Summarize these interviews and tell me what customers want. The output gives broad themes without evidence or priority.
[SOURCES AND CONSTRAINTS]List the materials the AI may use and rules it must follow.Use only 12 interview transcripts; group evidence by customer segment; do not treat one comment as a market-wide finding.

How to use

  1. Paste a real bad answer or current prompt; concrete failure gives the model something to diagnose.
  2. State the sources the tool can actually see, including any limits on web research, files, or company data.
  3. Use the high-stakes example when the output will drive a decision, be published, or affect a customer.
  4. Follow up with: “Stress-test this prompt against three likely failure cases for my task and revise only the instructions that prevent them.”

Variations

Prompt critique

Use this when you already have a prompt and need a precise revision.

Variation
Audit this prompt for [TASK]: [EXISTING PROMPT]. The intended reader is [AUDIENCE], and the model may use only [ALLOWED SOURCES]. Return a line-by-line critique identifying ambiguity, missing inputs, conflicting instructions, unsupported assumptions, and output requirements that cannot be checked. Then provide a revised version that preserves my intent and uses clear placeholders. Include a short test input and the acceptance criteria I should use to judge the answer. Do not add a persona unless it changes domain decisions. Check that every edit fixes an identified issue.

Team template

Use this when several colleagues need one standard prompt for repeat work.

Variation
Create a team prompt template for [REPEATED TASK], used by [TEAM] for [AUDIENCE]. Inputs currently available are [INPUTS], and the deliverable must follow [OUTPUT STANDARD]. Produce: a copy-ready master prompt, an input form with required versus optional fields, examples of a complete and incomplete submission, and a reviewer checklist. Include instructions for handling missing evidence and confidential information. Avoid steps that depend on undocumented company knowledge. Check that two people using the template would produce outputs with the same required sections.

Prompt test set

Use this when you need to evaluate prompts before rolling them out.

Variation
Build a small test set to evaluate prompts for [TASK]. The candidate prompt is [CANDIDATE PROMPT], and success means [SUCCESS CRITERIA]. Create five representative test cases covering normal input, incomplete input, conflicting source material, edge cases, and a request that should be declined or escalated. For each, provide the input, expected output characteristics, failure signals, and a scoring rubric from 0-2 per criterion. Do not invent proprietary facts; use neutral fictional examples. Check that every case measures a stated success criterion rather than writing style alone.

Tips

  • Give the AI a source boundary, such as “use only these interview transcripts,” whenever unsupported claims would matter.
  • Define quality in observable terms: “cite the interview ID beside each insight” is usable; “make it insightful” is not.
  • Separate required output sections from optional ideas so the model does not bury the important work in a long narrative.
  • Test a reusable prompt with incomplete and conflicting inputs; those cases expose missing uncertainty instructions quickly.

FAQ

Do I need to assign the AI a role in every prompt?

No. Add a role only when it implies real domain standards, such as a grant reviewer checking eligibility. Task inputs and acceptance criteria usually matter more.

Why does a detailed prompt still produce weak answers?

Length does not create a decision rule. The prompt may lack source boundaries, a defined deliverable, or a way to handle missing evidence.

Should I ask the model to ask questions first?

Ask it to ask only when a required input is missing. Otherwise it can delay straightforward work with unnecessary questions.

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