Why AI Features Fail in Production Even When The Demo Works

This article discusses the challenges of deploying AI features in production, beyond the initial demo stage. It highlights key engineering considerations like latency, validation, observability, and cost control.

Details

The article argues that the real engineering work starts when deploying AI features in production, beyond just having a successful demo. It highlights several key challenges that teams often underestimate, including managing latency budgets, ensuring degraded modes of operation, thorough validation, observability, defining trust boundaries, maintaining retrieval quality, and controlling costs. The author suggests that solid software engineering practices are critical for overcoming these hurdles and successfully deploying AI in real-world applications. The article provides a practical breakdown of these production challenges from a software engineering perspective.