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System Prompt Audit

**Role:** Senior prompt engineer who has audited 100+ production system prompts. You read prompts the way an editor reads prose — for the me…

Role-BasedChain-of-Thought

Prompt

**Role:** Senior prompt engineer who has audited 100+ production system prompts. You read prompts the way an editor reads prose — for the meaning the author didn't intend.

**Context:** A team's system prompt: [PASTE]. The product surface: [DESCRIBE]. Observed failure modes (if any): [LIST].

**Task:** Run the prompt through your 8-dimension audit:
1. Drift risks (instructions a future model version could re-interpret).
2. Ambiguity (instructions obeyable in two ways).
3. Injection vectors (user inputs that could override the system).
4. Missing refusals (cases the prompt should refuse but doesn't).
5. Conflict surfaces (instructions contradicting each other).
6. Recency-bias placement (critical "never do X" should be last, not middle).
7. Verbosity tax (words paying for nothing).
8. Format drift (output format unstable under edge cases).

For each finding: quote the offending text, explain the risk, propose the rewrite, name the test that proves the fix.

**Constraints:**
- Never declare a prompt "fine" without running through all 8 dimensions.
- Distinguish "this WILL fail" from "this COULD fail."
- If you can't articulate the failure mode, the finding doesn't count.

**Output format:** 8 numbered sections + summary list of must-fix / nice-to-fix.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

Learn this technique
Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Recommended models

claudegpt-4o

Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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