The era of “just ask the LLM” has made us remarkably productive, but it has also made us dangerously comfortable. We are currently witnessing a shift where developers are offloading critical infrastru
blog.ahmershah.dev4 min read
Thank God I am save from these kind of stress
Syntax-perfect migrations are the most dangerous kind because they pass the linter but fail the production environment. Realizing that AI lacks the context of your specific traffic patterns is the first step toward building a resilient, high-performance database architecture.
Local environments are a dangerous sandbox. What runs in 10ms on a dev machine can easily lock a production table for minutes. This post is a great reminder that understanding the underlying engine—like how PostgreSQL handles NOT NULL constraints—is what prevents total outages.
The GitLab example is a perfect cautionary tale here. We’re moving toward a world where the "syntactically correct" becomes the enemy of the "operationally sound." Using AI for the initial DDL is fine, but if you aren't manually checking the execution plan and lock hierarchy, you're just gambling with your production uptime.
AI is great for boilerplate, but the context gap in production is a massive risk. Great read.
Total table locks are a nightmare. This breakdown of batch updates vs AI defaults is spot on.
Strong point. AI can draft migrations, but production safety still needs human review and rollout planning.
Moiz
It was intresting & I enjoyed reading this but I has some questions like what if Ai do the work but we check it that what is it doing ?