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.
Because these pitfalls are common, if you’ve worked on any AI product, you’ve probably seen them before.
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?
Source: Chip Huyen — Read the full article on the original site ↗