Unverified50% confidenceOpinionExact time
The requirements of AI products are naturally vague, with unstable model outputs, uncertain user expectations, and rapidly changing technical boundaries; if the problem is not clearly defined during the research stage, all subsequent architecture design and development acceptance are built on quicksand.
1
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50%
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Long-term
Relevance
10/13/2026
First Seen
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Related Claims
UnverifiedBeginners commonly encounter three typical problems when describing requirements: vague descriptions, lack of structure, and neglect of edge cases and technical constraints—these are the main reasons AI output deviates from expectations.80% similarVerifiedThe fundamental reason why system design is difficult for AI to replace is that it involves a large number of trade-off decisions with no standard answers.78% similarUnverifiedFor AI features, because model inference costs are high and output results are difficult to exhaustively test, small-scale real-world usage can expose edge cases that cannot be discovered in a sandbox environment.76% similarUnverifiedThe absence of engineering judgment has a lagging effect: with AI assistance, junior developers can maintain relatively high output for extended periods without their capability gaps being immediately exposed.76% similarUnverifiedSystems architecture capability is difficult for AI to replace because it requires developers to make trade-offs under uncertainty: balancing performance against cost, weighing consistency against availability, and navigating the tension between technical debt and delivery speed76% similar
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