Many AI projects fail not because of weak models but due to overlooked fundamentals in design, evaluation, and deployment. Using real-world cases, this talk walks through the pitfalls of rushing from PoC to production: high costs, hallucinations, security risks, and lack of evaluation. Developers will learn practical strategies including prompt engineering, observability, guardrails, and cost control to build production-ready AI systems.