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LLM Over-Refusal Audit Rubric

Audit your LLM for over-refusal: classify false rejections across a 7-category taxonomy, score borderline cases with calibrated anchors, map helpfulness-safety tradeoffs, and get a prioritized remediation plan — for ML and product teams in healthcare, legal, and customer support.

A 4-step agentic workflow pack for other built to run with ChatGPT, Claude, and Gemini. Open the Markdown files, fill the variables, and paste into your model. Most buyers get a reviewable result in about 45 minutes.

  • Classify every false refusal across a 7-category taxonomy with severity and frequency scoring
  • Score ambiguous borderline cases with calibrated 1–5 anchors that encode domain-specific judgment
  • Map each refusal pattern onto a helpfulness-safety tradeoff matrix to find systematic policy miscalibration
  • Receive a prioritized remediation plan with specific prompt-engineering, fine-tuning, and policy levers
  • Designed for high-stakes domains: healthcare triage, legal research, customer support, and dual-use scenarios
  • Encodes judgment from FalseReject literature and 2025 over-refusal benchmarks — not producible by a naive ask
CChatGPTClaudeClaudeGeminiGemini
promptscart.com / prompt-packs / llm-over-refusal-and-refusal-audit-rubric
Run in
ChatGPT · Claude +1
Your AI model
Step 1
False-Refusal Classifier Rubric
Paste your sampled refusal log and domain context — the rubric classifies each refusal into one of 7 false-refusal categories with severity and confidence scores.
Step 2
Borderline Case Scorer
Feed the borderline cases surfaced by the classifier rubric plus your domain policy — each case is scored 1–5 with explicit anchor justification and a recommended label.
Step 3
Helpfulness-Safety Tradeoff Mapper
Feed the classifier findings and borderline scores — the mapper plots every refusal pattern across a helpfulness-safety matrix and names the dominant miscalibration zone.
Step 4 · optional
Remediation Recommendation Prompter
Feed the tradeoff mapper output and your team's constraints (model access level, fine-tuning availability, deployment timeline) — the prompter produces a ranked remediation backlog.
Output
Your deliverable
Copy-paste ready
One-time
$8
~6 hrs / week
time back

Prompt Customization Serviceoptional help adapting variables and output to your brand voice. Choose your tier at checkout (not tied to this prompt's price).

Instant download after payment
Refund as per the Refund Policy.
Email Support · 24h SLA
Lifetime updates

Models supported
C ChatGPTClaude ClaudeGemini Gemini
Best valueSave $786
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Download LLM Over-Refusal Audit Rubric

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What ships with your purchase

Prompt files

Plain Markdown files with `{{variables}}` you fill in, ready to paste into ChatGPT, Claude, or Gemini. No setup, no tooling required.

Usage guide

Variable reference, model compatibility, examples, and customization tips so you can adapt the pack to your brand voice.

Lifetime updates

When we improve the pack, you get the new version automatically. Email support included with every purchase.

Models tested: ChatGPT, Claude, Gemini.

The workflow inside this pack

4 composable prompts you run in order — each one picks up where the last left off.

  1. Step 1

    False-Refusal Classifier Rubric

    Paste your sampled refusal log and domain context — the rubric classifies each refusal into one of 7 false-refusal categories with severity and confidence scores.

  2. Step 2

    Borderline Case Scorer

    Feed the borderline cases surfaced by the classifier rubric plus your domain policy — each case is scored 1–5 with explicit anchor justification and a recommended label.

  3. Step 3

    Helpfulness-Safety Tradeoff Mapper

    Feed the classifier findings and borderline scores — the mapper plots every refusal pattern across a helpfulness-safety matrix and names the dominant miscalibration zone.

  4. Step 4 · optional

    Remediation Recommendation Prompter

    Feed the tradeoff mapper output and your team's constraints (model access level, fine-tuning availability, deployment timeline) — the prompter produces a ranked remediation backlog.

Perpetual (lifetime) use license

Your one-time purchase includes an ongoing right to use this prompt pack with the AI tools and models you control for your own and your clients' work — not for resale or public redistribution of the files as a product.

We keep the copyright

The prompt files, guides, examples, and bundled assets stay our copyrighted works (or our licensors'). Payment grants the limited license in our Terms only — it does not transfer ownership.

Need help adapting this prompt to your team? Add Prompt Customization Service at checkout.

FAQ

How long does it take to use LLM Over-Refusal Audit Rubric?
Most buyers finish in a few minutes: open the prompt file, fill the variables, and paste into your model. The first run is the slowest because you decide variable values; reuse is instant.
What if I get stuck?
Email support@promptscart.com. Free basic support is included with every purchase, and you'll get a reply from our team within 24 hours. If you need help adapting variables or output, we can schedule a call.
Do I need a paid plan with ChatGPT?
The prompt works on free tiers of ChatGPT, Claude, and Gemini. Heavy use can hit free-tier limits; paid plans get longer context and faster responses, but the prompt itself is the value.
Can I customize the prompt?
Yes, completely. You own the prompt files: edit the role framing, add variables, swap output sections, fork it to match your brand voice. Support can help you plan customizations over email.
What if it doesn't work for me?
Refund as per our Refund Policy (https://promptscart.com/refund-policy). Or add Prompt Customization Service at checkout for help adapting variables and output to your workflow.