A Red Teamer's View: 3 High-Capability Prompt Patterns That Work Across Models
Three patterns shared by the HackAPrompt 2.0 champion: frame the task as an audit, make the abstraction level explicit, and stage the context like production. They work on Claude, GPT, and Gemini alike.
3 patterns: (1) Frame task as audit of itself (2) Pin abstraction level explicitly (3) Stage context the way it arrives in production
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The original author is an AI red-teamer and HackAPrompt 2.0 champion. These 3 patterns aren't academic theory; they're repeatable techniques proven in hands-on cross-model red teaming. They hit the exact pain point of "my prompt looks OK but the results are just average." You can apply them to any existing prompt without a rewrite.
AI output 從 average tutorial 級跳到 production-grade — 命名、edge case、type hints、doc strings 全部上一個檔次；audit 段會出現「真正的答案」；production-shape 輸入測過、不會「測試 work 上線壞」
A description of the intended result. Check this entry's testing notes and try the prompt with your own inputs.
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