Common Pitfalls When Building Generative AI Applications
· 10 min read
Most generative AI projects fail — not because the models are bad, but because teams make the same predictable mistakes at every layer of the stack. A 2025 industry analysis found that 42% of companies abandoned most of their AI initiatives, and 95% of generative AI pilots yielded no measurable business impact. These aren't model failures. They're engineering and product failures that teams could have avoided.
This post catalogs the pitfalls that kill AI projects most reliably — from problem selection through evaluation — with specific examples from production systems.
