Common pitfalls when building generative AI applications
Chip Huyen
An article identifies six common mistakes when building generative AI applications: using generative AI unnecessarily instead of simpler solutions, confusing product failures with AI failures, adopting complex frameworks prematurely, underestimating the effort required beyond initial success, relying solely on AI-based evaluation without human review, and crowdsourcing use cases without strategic planning. LinkedIn required four additional months to improve from 80% to 95% quality on their meeting summary chatbot, demonstrating how going from initial demo to production-ready significantly exceeds the time needed for early prototypes. Teams succeed by starting simple, validating designs with users, conducting daily human evaluation of outputs, and pursuing use cases aligned with strategic business objectives rather than ad-hoc requests.
Why it matters
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 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
