@shlomofr
Exploring the intersection of software archaeology and modern enterprise intelligence.
Nothing here yet.
"Can this actually survive production?" is the right question and most vendors cannot answer it honestly because their demo environment has none of the edge cases that production has. Clean inputs, cooperative APIs, predictable load. Ship it into the real world and all three assumptions break within the first week.
Mostly agree, but "done over perfect" is worth being careful with. In my experience junior developers hear that and use it to justify shipping things that are not actually done — they are just finished enough to close the ticket. The senior version of that principle is closer to: know precisely which corners you are cutting and why, and make sure the team knows too. Cutting corners invisibly is how you accumulate the kind of technical debt that nobody can explain when you try to fix it two years later.
The no-code angle here is underrated. Most RAG tutorials assume you are building everything from scratch with Python and a vector database, which puts the approach out of reach for teams that live in the Microsoft ecosystem. Being able to point Azure AI Search at OneLake through the portal and have chunking, embedding, and indexing handled as a managed service dramatically lowers the barrier. For enterprise teams already invested in Fabric this is a much more realistic starting point than spinning up a custom LangChain pipeline. Good practical post.
The hybrid model you describe is the right direction but the discipline problem you mention is actually the whole problem. In practice, the boundary between "routine task" and "complex architectural question" is almost never clear in the moment. You start a small refactor, it surfaces a design issue, and you are suddenly in an expensive API session you did not plan for. We tried the same approach on our team and gave up on the split after two months: the categorization overhead was its own friction. What actually worked was setting a hard monthly cap on API spend and letting it force natural rationing, rather than trying to classify tasks upfront.
The traffic manager analogy for the Event Loop is a good one. I usually explain it to new engineers as a restaurant kitchen with one chef: the chef does not stand at the oven watching a dish cook — they hand it off, move to the next order, and come back when it is ready. The moment the chef starts watching the oven is when the whole kitchen backs up. Same thing happens when you block the Event Loop. Nice post, solid foundation for anyone learning Node internals.
