Verified65% confidenceFactExact time
Prompt engineering has inherent limitations: the effectiveness of instructions decays over conversation turns (instruction forgetting), and different model versions respond significantly differently to the same instruction.
3
Sources
65%
Confidence
Long-term
Relevance
7/11/2026
First Seen
Sources
ChatGPT说「我」：AI拟人化设计的机遇与隐忧
redditr/ChatGPT7/10/2026
Related Claims
UnverifiedCurrent model instruction-following failures commonly manifest as ignoring system prompt constraints, forgetting early instructions in long conversations, randomly violating formatted output requirements, and deviating from predefined processes in multi-step tasks.80% similarUnverifiedLong prompts suffer from execution decay: the more constraints there are, the greater the deviation between actual output and textual description, and a more detailed prompt does not equal more accurate model execution.79% similarUnverifiedPrompt engineering remains an empirical technique rather than an exact science; different models respond very differently to the same prompt, and model updates may render old prompts ineffective.79% similarUnverifiedPrompt工程存在提示漂移问题，随着对话轮次增加早期的风格约束会被逐渐稀释76% similarVerifiedIncremental Prompting can reduce hallucinations and logical errors in large language models when processing complex instructions76% similar
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