Production AI is not a demo: lessons learned building real GenAI systems with MongoDB

In the last year, I’ve seen a pattern repeat itself again and again.

AI demos look impressive.
Production AI systems behave very differently.

Once you move beyond notebooks and prototypes, the real challenges emerge: data consistency, latency, hybrid search, lifecycle management, and the uncomfortable gap between “it works” and “it works reliably”.

While working on real-world AI platforms (public sector, large document repositories, search-heavy systems), I ended up converging on a few hard truths:
• Retrieval is the core of GenAI, not the model
• Vector search alone is rarely enough
• Data architecture matters more than prompt engineering
• Production AI fails quietly when observability is ignored

MongoDB turned out to be a surprisingly strong foundation for these systems, not because of hype, but because it sits naturally at the intersection of operational data, search, and AI workloads.

I recently wrote a deeper piece where I walk through:
• what “production AI” actually means
• why hybrid search (full-text + semantic) is essential
• how to structure data and embeddings without painting yourself into a corner
• the architectural mistakes I see teams repeat

I expand these ideas in more detail in Production AI with MongoDB

This is not a tutorial and not marketing.
It’s a field report from systems that had to survive real users, real data, and real constraints.

If you’re building GenAI systems meant to last longer than a demo, I hope it helps you avoid a few expensive mistakes.