Every few years, a new abstraction arrives that makes the previous one feel embarrassing in retrospect.

Raw SQL gave way to ORMs. Callback hell gave way to async/await. Manual memory management gave way to garbage collection. Each shift wasn't just about writing less code — it was about thinking differently about the problem.

We're at one of those moments with AI.

The way we learned to talk to LLMs

Most AI-powered applications today follow the same pattern:

Write a prompt string

Interpolate some variables into it

Call the API

Parse the response (pray it's valid JSON)

Map it to your data model

Validate

Handle the cases where it isn't valid

Retry

This is prompt-centric thinking. The prompt is the program. The data model is just a container waiting at the end of a pipeline.

It works.…