Common pitfalls when building generative AI applications
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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. 1. Use generative AI when you don't need generative AI Every time there’s a new technology, I can hear the collective sigh of senior
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Chip Huyen catalogs the failure modes she's watched teams hit repeatedly—starting with the big one: reaching for generative AI when a simpler solution would do. It's a field guide to the mistakes that don't show up until you're already committed. Read it before you scope your next LLM feature, and you'll dodge the traps that quietly sink AI products in production.
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