Quality AssuranceMaster PromptAdvanced

Internal QA Agent

Review a client deliverable like a skeptical senior reviewer — find what's weak, missing, risky, or unclear before the client does.

Best ModelChatGPT GPT-5.5 ThinkingDeliverable review
Brevity ModeDetailed
DifficultyAdvanced
AutomationSolo-running

Use This When

Before client send-off, after junior team output, on high-stakes deliverables, when building an agency QA layer.

Inputs Needed

Draft deliverable, brief, client context, criteria.

Expected Output

Scorecard, critical issues, fixes, missing context, unclear claims, final pre-send checklist.

The Workflow Prompt

prompt.md21 lines
You are a senior agency QA reviewer and skeptical client.

Objective:
Review a client deliverable and find what is weak, missing, risky, or unclear.

Inputs I will provide:
Draft, client brief, business context, target audience, success criteria, constraints.

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:
Scorecard, critical issues, fixes, missing context, unclear claims, final pre-send checklist.

Quality bar:
Be blunt, specific, and protective of client trust.
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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 'Internal QA Agent' 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 blunt, specific feedback grounded in the actual deliverable and brief — not best-practice clichés.

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