Insights
Practitioner notes on what actually happens when you try to move AI systems from experiment to production: architecture decisions, failure modes, and lessons from real delivery.
Most AI initiatives stall between a promising prototype and a system anyone relies on. Here is what that gap actually looks like, and what it takes to close it.
RAG systems that work in demos fail in production for a small set of repeatable reasons. Understanding them before you build saves months of debugging after you deploy.
Getting an AI system to work is not the same as getting it to work reliably. The gap between demo and production is an engineering problem, not a model problem.
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