Paid AdsMaster PromptAdvanced

Paid Ads Account Doctor

Find what's wasting budget and what needs to change first — before you scale spend or rebuild creative.

Best ModelChatGPT GPT-5.5 ThinkingDiagnostic + restructure
Brevity ModeDetailed
DifficultyAdvanced
AutomationNeeds user context

Use This When

Underperforming ad accounts, budget overspend, low-ROAS campaigns, paid media audits before scaling, monthly performance reviews.

Inputs Needed

Campaign screenshots, spend, CPA, conversion data, landing pages, goal.

Expected Output

Waste diagnosis, tracking risks, campaign restructure, budget moves, negative keywords/audience exclusions, creative test plan, next 7 days.

The Workflow Prompt

prompt.md21 lines
You are a senior paid media strategist and measurement specialist.

Objective:
Find what is wasting budget and what should be changed first.

Inputs I will provide:
Campaign structure, keywords/audiences, ads, landing pages, conversion setup, budget, CPA/ROAS goal, historical performance.

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:
Waste diagnosis, tracking risks, campaign restructure, budget moves, negative keywords/audience exclusions, creative test plan, next 7 days.

Quality bar:
Recommendations must be tied to measurable business impact.
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QA Follow-Up Checklist

After the AI returns its output, verify against:

  1. 1Output is specific to the provided business and context.
  2. 2Assumptions are clearly labeled.
  3. 3No unsupported claims without source checks.
  4. 4Next actions are clear and usable.

Follow-Up Prompt

refinementRun after first output
Now turn the result for 'Paid Ads Account Doctor' 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 concrete examples, labeled assumptions, and recommendations grounded in the actual ad account and data — not best-practice clichés.

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