Unverified50% confidenceOpinionExact time
The upper limit of AI output quality is largely determined by the information density of the prompt, and vague instructions can only yield generic answers.
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Long-term
Relevance
10/19/2026
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Unverified用字符数作为提示质量的代理指标捕捉了信息密度这一维度，但并非完美的质量度量74% similarUnverifiedThe more vague the question, the more the model tends to output statistically common, mediocre answers; the more precise the constraints, the closer the output is to the specific requirement.74% similarUnverifiedResearch shows that the cognitive load of code review has a nonlinear relationship with the size and complexity of code changes, and review of large-scale AI-generated code easily leads to perfunctory approvals.73% similarUnverifiedLarge language models are prone to hallucinations when handling complex and ambiguous requirements; the larger and more ambiguous the scope, the higher the probability of AI errors, so reducing scope for MVP lowers the risk of AI mistakes.73% similarUnverifiedThe net benefit of AI code review depends not on recall but on signal-to-noise ratio and downstream processing capacity; if the false-positive rate is high, teams instead spend more time filtering out noise.72% similar
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