LLM System Design Checklist: 7 Things I Wish Every AI Engineer Knew Before Building an AI App

Building with large language models feels deceptively simple. You call an API, send a prompt, get a response. Ship it. But once your AI feature hits real users, things break quickly. Responses become inconsistent. Context overflows. Costs spike. Outputs drift. Users lose trust. After studying and experimenting with LLM-based systems, I’ve realized that most failures don’t come from model limitations — they come from weak system design. Here’s the checklist I wish every AI engineer had before building an AI-powered application. 1. Define Identity Before You Define Prompts Most developers start with prompts. That’s backwards. Before designing prompts, define: What role does this AI play? What constraints must it follow? What tone and reasoning style should remain consistent? What must it never do? Without identity anchoring, your system will produce inconsistent outputs. One session it sounds strategic. The next it contradicts itself. Identity is not just a system message — it’s a design