Python dependency management is not a developer problem. It is a team productivity problem that shows up as slow CI, painful on-boarding, and a different tool in every repository.
This article is for the ones building an LLM product and want to know what it actually costs you - per feature, per user, per request - without paying for Datadog or wiring up OpenTelemetry.
We will walk through a concrete RAG example - a pipeline over a corporate annual report - and build the testing layer that most teams skip entirely. The code is real and runnable. The failures are not hypothetical.
Your Python tools need persistence. Here's how to add it without spinning up a database server, using TinyDB, a zero-dependency document store that lives in a single JSON file.
If you've written a decorator before, you know the syntax. But let's slow down and look at what Python is actually doing, because that mental model is the key to understanding parametrized decorators later.