Common pitfalls when building generative AI applications

Chip Huyen · 1 year ago

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.