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Common pitfalls when building generative AI applications

Named practitioner synthesis

#190

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Practitioner Notes
First seen
2026-07-16 19:08:00
Last seen
2026-07-16 19:08:00

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  • Blog / Newsletter2026-07-16 19:07:19
    Common pitfalls when building generative AI applications

    <p>As we’re still in the early days of building applications with foundation models, it’s normal to make mistakes. This is a quick note with examples of some of the most common pitfalls that I’ve seen, both from public case studies and from my personal experience.</p> <p>Because these pitfalls are common, if you’ve worked on any AI product, you’ve probably seen them before.</p> <h2 id="1_use_generative_ai_when_you_don_t_need_generative_ai">1. Use generative AI when you don't need generative AI</h2> <p>Every time there’s a new technology, I can hear the collective sigh of senior engineers everywhere: “Not everything is a nail.” Generative AI isn’t an exception — its seemingly limitless capabilities only exacerbate the tendency to use generative AI for everything.</p> <p>A team pitched me the idea of using generative AI to optimize energy consumption. They fed a household’s list of energy-intensive activities and hourly electricity prices into an LLM, then asked it to create a schedu