A common mistake when building AI features is assuming that a working LLM demo is close to a production product.
In reality, the gap is huge.
A production AI product needs more than prompt engineering. It needs strong data foundations, context and memory, model gateway and routing, safety and privacy controls, evaluation pipelines, observability, cost optimization, CI/CD, human-in-the-loop workflows, and governance.
I created this infographic as a practical checklist for developers and engineering teams building AI-powered products. It summarizes the 12 layers between an AI demo and a real production AI system.
The goal is simple:
Build AI products that are not only impressive in a demo, but also reliable, secure, observable, cost-efficient, and maintainable in production.
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