AI Engineering cookbook

You want to build something ambitious, and you have no idea if it will bankrupt you at scale.

Everyone says self-hosting saves money at scale, and you have no idea what scale means for your app.

You built something real, and when someone asks you to walk through the architecture out loud, you freeze.

Everyone reaches for an agent by default, and half the time a plain script would work better and cost less.

Model too big for your GPU, budget too small for the model.

An agent demo in an afternoon. The real work turns out to live somewhere else.

Almost nobody clicks thumbs-down, and you still need to know when your app is actually failing people.

Your health check says everything's fine while the model behind it hasn't even finished loading yet.

Your vector database has one bad five minutes, and your whole app goes down with it.

Every part of your app calls the model directly, so a rate limit, an outage, or a repeated question costs you separately each time.

Your support agent keeps a confused customer talking for ten more turns instead of just handing them to a person.

One customer asks a question. The answer comes back holding a piece of a different customer's document.

Your load test passed with clean numbers, and real users still hit long stalls the moment you shipped.

Your agent dies halfway through a long task, and the only way to recover is to start the whole thing over from scratch.

You know RAG, agents, and evals separately, and you have never actually put all three in one script that runs end to end.