AI product teardowns: how real AI products are probably built
Each teardown reasons about how a type of AI product is likely built, from what it does in public, what its makers have published, and how production systems are generally designed. None of it is insider knowledge, so treat each one as a design exercise you can check against the product's own behaviour.
Why teardowns help
You use these products every day, so you can test a hypothesis about their design yourself: send a long conversation and watch it slow down, ask the same question twice and compare, watch when a search step appears. That turns system design from diagrams into something you can observe, and it's good practice for system design interviews.
Teardowns
| Teardown | Teaches |
|---|---|
| What happens when you send a message to a chat assistant | The full request path: gateway, context, safety, routing, tools, inference |
| How an AI answer engine with citations is likely built | RAG over the web, and why source quality matters |
| How an AI coding agent is likely built | Tools, loops, context management and permissions |
How to read one
Each teardown gives a likely architecture, what you can observe yourself to check it, and where that kind of product fails. When a company has published details, the page links to them, and everything else is labelled as reasoning.
From the floor
These teardowns follow the same zones as the production inference pipeline I teach, where we trace one question, "what is the delivery status of my order, and what's your policy if it arrives damaged?", through every zone, because it has two subtasks, a database lookup and a policy question, which shows how a real request splits into steps.