Prompt EngineeringMaster PromptAdvanced
AI Prompt Optimizer
Rewrite a weak prompt into a reusable professional prompt — with variables, output format, and QA tests so a junior team member can run it.
Best ModelChatGPT GPT-5.5 Thinking / Claude Opus 4.7Prompt refactor
Brevity ModeDetailed
DifficultyAdvanced
AutomationSolo-running
Use This When
When a prompt keeps misfiring, when scaling AI workflows across a team, when building internal tooling, when productizing an agency workflow.
Inputs Needed
Weak prompt, tool/model, desired output, constraints.
Expected Output
Improved prompt, variables, instructions, output format, QA tests, follow-up prompts.
The Workflow Prompt
prompt.md
You are a prompt engineer and QA architect. Objective: Rewrite a weak prompt into a reusable professional prompt. Inputs I will provide: Original prompt, intended user, model/tool, desired output, constraints, examples, failure modes. Instructions: - Start by identifying the business objective and the likely leverage point. - Ask up to 5 focused questions only if required. If enough context exists, proceed and label assumptions. - Produce client-ready work, not generic advice. - Use concrete examples, templates, and priority order. - Mention risks, dependencies, and what must be verified before launch. - End with a short QA checklist and next 3 actions. Required output: Improved prompt, variables, instructions, output format, QA tests, follow-up prompts. Quality bar: The improved prompt should be reusable by a junior team member.
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QA Follow-Up Checklist
After the AI returns its output, verify against:
- 1Output is specific to the provided business and context.
- 2Assumptions are clearly labeled.
- 3No unsupported claims without source checks.
- 4Next actions are clear and usable.
Follow-Up Prompt
refinement
Now turn the result for 'AI Prompt Optimizer' into a client-ready version: tighten wording, remove fluff, add missing assumptions, and provide the next 3 actions.
Avoid / Cautions
Avoid generic output. Push the model for specific variables, test cases, and structure grounded in the actual workflow — not best-practice clichés.
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