Building LLM Applications for Production ↗

Editorial Notes

The definitive technical due diligence checklist for AI systems, written by one of the most credible voices in ML production (Chip Huyen, Stanford CS 329S instructor and author of “Designing Machine Learning Systems”). Her key thesis — “it’s easy to make something cool with LLMs, but very hard to make something production-ready” — is exactly the framing technical leaders need when evaluating their team’s AI proposals. The article covers the full spectrum of production concerns: prompt ambiguity and versioning, evaluation strategies, cost analysis (with real numbers), latency challenges, the prompting vs fine-tuning decision framework, and testing strategies. For technical PMs and CEOs reviewing engineering proposals, this provides the vocabulary and checklist for asking the right questions: Has the team addressed evaluation? What’s the cost-per-query at scale? What’s the latency budget? How are they handling prompt versioning? Use this as your technical due diligence framework before approving AI projects for production.