Author(s): Anthony Usoro
Originally published on Towards AI.
From choosing the right LLM to shipping a production RAG pipeline — the practical path, not the demo-day version.
Building an AI demo takes an afternoon. Deploying AI that survives real users, real data, and real failure modes takes considerably more discipline. Most of the AI projects that stall inside a business don’t fail because the underlying model was weak — they fail because the gap between “it worked in my notebook” and “it works in production” was never properly planned for.
This guide walks through that gap step by step: how to decide what kind of AI system you actually need, how to build it, and how to deploy it in a way that holds up once real customers or employees depend on it.
