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    AI software needs a clear distinction between problems and solutions, one post argues

    The post warns against expecting AI to define the problem for you or overengineering brittle implementations for a specific language model.

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    1 Source, 524d ago, first seen 524d ago

    TLDR

    A post argues that good AI software requires a clear understanding of problems versus solutions. It warns that expecting AI to handle problem specification will leave you dissatisfied, while overengineering brittle implementation choices for a particular large language model will prevent your software from scaling.

    Combined views

    8.5K

    1 Source, first seen 524d ago

    Combined views

    8.5K

    1 Source, first seen 524d ago

    77 likes
    77 likes
    2 comments
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    9 reposts

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    2 comments
    28 saves
    9 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

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    1 Source

    @lateinteractionBuilding good AI software requires a crisp mental model of problems vs solutions. If you assume that AI will do the problem specification for you, you won't be satisfied. If you overengineer brittle implementation choices for a specific LLM, your software won't scale.

    1 Source

    @lateinteractionBuilding good AI software requires a crisp mental model of problems vs solutions. If you assume that AI will do the problem specification for you, you won't be satisfied. If you overengineer brittle implementation choices for a specific LLM, your software won't scale.