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

Common pitfalls when building generative AI applications

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2025-01-17 10:00:05

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.

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.

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 schedule to minimize energy costs. Their experiments showed that this could help reduce a household’s electricity bill by 30%. Free money. Why wouldn’t anyone want to use their app?

I asked: “How does it compare to simply scheduling the most energy-intensive activities when electricity is cheapest? Say, doing your laundry and charging your car after 10pm?”