Series 12 parts

Build Production LLM Apps with Python & FastAPI

A hands-on, 12-part track that builds from modern Python through FastAPI and into production LLM features, with runnable code in every part.

This series builds one skill at a time. You start with a modern Python toolchain, move into FastAPI for production APIs, and finish by wiring real language model features: reliable JSON, retrieval, streaming, and a small agent. Every part ships runnable code, and most include a playground so you can run Python right in the browser.

In this series

  1. 1 Modern Python 3.14 Setup for LLM Projects: uv, Virtualenvs, Typing, and Project Layout Set up a fast, reproducible Python 3.14 project with uv, a src layout, Ruff, and mypy: the foundation for the FastAPI and LLM work ahead.
  2. 2 Type-Safe Data Modeling with Pydantic v2 and Python Type Hints Use Pydantic v2 to validate data at the boundary: models, field constraints, custom validators, and clean serialization, the backbone of FastAPI and reliable LLM output.
  3. 3 Async Python Deep Dive: asyncio, httpx, and Concurrency for API and LLM Calls Understand asyncio and the event loop, then fan out API and LLM calls concurrently with httpx, gather, and a semaphore to bound load.
  4. 4 FastAPI Fundamentals: Routing, Pydantic Models, and Dependency Injection Build real FastAPI endpoints: typed routing, Pydantic request and response models, dependency injection, and automatic docs.
  5. 5 FastAPI in Production: Settings, Auth, Middleware, and Project Structure Harden a FastAPI app for production: typed settings with pydantic-settings, bearer auth, logging and CORS middleware, and a scalable project structure.
  6. 6 Streaming and Background Work in FastAPI: SSE and BackgroundTasks Stream responses from FastAPI with server sent events, run side effects with BackgroundTasks, and know when to move to a real task queue.
  7. 7 Testing FastAPI the Right Way: pytest, the Test Client, and Validation Test FastAPI with pytest and the test client: assert on validation, override dependencies to isolate from real services, and cover async and streaming code.
  8. 8 Calling LLMs from Python: The Request Loop, Tokens, Cost, and Retries Call language models from Python with the Claude SDK: the messages loop, tokens and cost, and a client with timeouts and retries you can inject and test.
  9. 9 Structured Outputs and Function Calling: Getting Reliable JSON from LLMs Get dependable JSON from language models with structured outputs and function calling, then validate with Pydantic so your code works with typed objects.
  10. 10 Building a RAG Service with FastAPI: Chunking, Embeddings, and Vector Search Build a RAG service end to end: chunk documents, embed and search by similarity, and answer grounded in retrieved context from a FastAPI endpoint.
  11. 11 Streaming LLM Responses to the Browser with FastAPI and SSE Stream a real model response to the browser: consume the model stream in Python, forward it through a FastAPI SSE endpoint, and render it live.
  12. 12 Building an AI Agent API: Tool Calls, Memory, and Guardrails Build a small AI agent API: the tool calling loop, conversation memory, and the guardrails that keep an action taking agent safe and bounded.