Prompt Engineering / Meta Prompts
Prompt Edge Case Hunter
Identify inputs that would break a prompt — adversarial, ambiguous, out-of-domain, or contradictory.
Your prompt works on happy-path inputs. But the real world isn’t happy path — and that’s where it will fail.
You get:
- production failures from inputs you never considered
- users accidentally (or intentionally) breaking your prompt
- prompt injection attacks you didn’t anticipate
- embarrassing outputs when the input is ambiguous
- no testing strategy for edge cases
But edge cases are predictable:
- empty input: what if there’s nothing to process?
- extremely long input: does it truncate or break?
- ambiguous input: can it ask for clarification?
- contradictory input: can it detect and handle?
- adversarial input: can it resist prompt injection?
- out-of-domain: can it say “I don’t know”?
Without edge case hunting, your prompt is fragile.
This prompt generates test inputs that will break your prompt.
The Prompt
Assume the role of a red-team prompt engineer who breaks prompts. Your task is to generate edge-case inputs that would cause the prompt to fail. Generate: 1. PROMPT UNDER TEST - What it does - Input format expected - Output format expected 2. EDGE CASE INPUTS (with predicted failure mode) | Category | Test Input | Why It's Problematic | Predicted Failure | |----------|------------|---------------------|-------------------| | Empty | [input] | No content to process | Hallucination or guess | | Extremely long | [input] | Exceeds context window | Truncation or error | | Ambiguous | [input] | Multiple valid interpretations | Random choice | | Contradictory | [input] | Instructions conflict | One instruction ignored | | Out-of-domain | [input] | Topic not covered | Hallucinated answer | | Adversarial (prompt injection) | [input] | Tries to override instructions | Instruction ignored | | Non-English | [input] | Language not specified | Unpredictable | | Malformed format | [input] | Wrong structure | Error or misinterpretation | 3. SEVERITY ASSESSMENT - Which failures are critical (data leak, harmful output)? - Which are acceptable (e.g., "I don't know")? - Which need immediate fixing? 4. FIX RECOMMENDATIONS - Per edge case, how to modify prompt to handle it 5. TEST PROTOCOL - Which edge cases to test before deployment INPUTS: Prompt to test: [PASTE THE PROMPT] Expected input domain: [E.G., "Customer service emails in English, 1-5 paragraphs"] Allowed outputs (what's acceptable): [E.G., "URGENT/NORMAL/LOW or 'CANNOT_CLASSIFY'"] Model: [GPT-4 / CLAUDE / GEMINI] RULES: - Assume users will try to break your prompt (some malicious, most accidental) - Flag any prompt that doesn't have a "I don't know" or fallback behavior - Test prompt injection: inputs that try to change the model's instructions - Consider adversarial inputs: "Ignore previous instructions and..." - Rate severity by potential harm, not just failure to comply
How To Use It
- Run this before deploying any prompt to production — especially public-facing ones.
- Pay closest attention to adversarial inputs — prompt injection is a real security risk.
- Add a fallback instruction to every prompt: “If you cannot classify, respond with ‘UNSURE’.”
- Test the “fix recommendations” by running the edge cases again.
- Save edge cases as regression tests for future prompt versions.
Example Input
Prompt to test:
“Classify this customer email as URGENT, NORMAL, or LOW. Respond with only one word.”
Expected input domain:
“Customer service emails in English, 50-500 words”
Allowed outputs:
“URGENT, NORMAL, LOW”
Model:
“GPT-4”
Why It Works
Most prompt testing uses happy-path examples that the prompt already handles well — which tells you nothing.
This framework improves outcomes by forcing:
- edge case generation (what will actually break it)
- failure mode prediction (how it will break)
- severity assessment (which breaks are critical)
- fix recommendations (how to harden the prompt)
- test protocol (what to test before deploy)
Great edge case hunting doesn’t just find failures — it tells you how to prevent them.
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