Clear lessons on AI workflows, engineering, and digital products
Enterprise RAG sounds simple on paper: connect documents, retrieve relevant context, send it to an LLM, and return an answer. In practice, that is not what separates a convincing demo from a system a business can trust. A real enterprise RAG solution has to deal with changing data, fragmented tools,
The AI agent framework market has gone from a Cambrian explosion to something much harsher: a survival test. In the first wave, almost every new framework looked exciting because almost every demo looked magical. By 2026, that phase is over. Teams are no longer choosing based on who can produce
So far in this series, we have built a NestJS API that can: * define routes * validate input * organize logic cleanly * return JSON responses * feel like a real backend Very nice. But there is still one big problem: the data is probably living in memory Which is totally fine for learning.
At the beginning, a NestJS app feels pretty manageable. You have: * a few modules * a few routes * some DTOs * a service or two * maybe a little auth * maybe some Swagger * and life feels reasonably calm Then the app grows. And suddenly: * modules start depending on each other too much * business
Once a NestJS app starts growing, a very specific kind of curiosity shows up. It usually sounds like this: * “Should we split this into microservices?” * “Maybe this module should be its own service.” * “This architecture diagram would look way cooler with message queues.” * “Surely this is the point where everything
