02 of 12 - Python for LLM Applications

The model is the easy part. The Python around it is where most LLM projects quietly die — wrong abstractions, blocking calls, untyped APIs no other service can trust. This video is the production Python baseline for building LLM apps: the language-level decisions that decide whether your app is a service or a demo. Nobody hires a model-caller. They hire a stack owner — and the stack starts with Python done right. 📌 WHAT YOU'LL LEARN • Type hints as the contract — the client of your endpoint is software, not a person • Dependency management that survives a model upgrade: venvs, uv, and pinning • Async I/O that actually matters — asyncio, httpx, anyio — and why model calls need it • FastAPI + Pydantic: the endpoint and schema layer that makes your app consumable • Streaming responses and queues for long-running jobs — the shape of a real LLM endpoint • The testing ladder for LLM code: unit → integration → golden sets (RAGAS, LLM-as-judge) • The production pipeline in order: ruff, mypy, pytest, eval gate, Docker — with a CI determinism rule If you're about to write Python for a real LLM service, watch this before you write your first endpoint. 📚 THE SERIES "Building Production AI, LLM & Agentic Apps" is a 12-part series that walks the entire stack, one layer at a time. By the end you'll be able to name every layer of a production AI app, build the AI features (prompts, structured outputs, retrieval, agents), build the app around them the way an enterprise expects, and explain it to the people who sign off. Next up — Video 3: LLM Integration, Prompting & Model Selection.