Module 05
Structuring End-to-End LLM Applications
Turn a script that works on a good day into an application that survives a bad one. You will validate model output with Pydantic and structured outputs, handle every kind of failure, run thousands of requests concurrently or through batch APIs, and manage rate limits and token budgets. Then you will put it all behind a FastAPI service with streaming, and give your team a quick Streamlit or Gradio chat page.
By the end of this module you can
- get typed, validated data out of a model with Pydantic and structured outputs, and check it against real records.
- sort AI failures into four families and handle each with retries, fallbacks or alerts, so users never see a stack trace.
- process thousands of requests with asyncio and a semaphore, or at half price with a provider Batch API.
- retry rate-limited calls with exponential backoff and jitter, and keep requests inside a token budget.
- build async FastAPI endpoints that validate input, map model errors to clear HTTP responses and stream replies.
- build an internal chat UI in Streamlit or Gradio in under an hour.
Lessons
7 in this module- 01JSON outputs and structured response handlingTurn unreliable model text into trusted, typed data using defensive parsing, Pydantic validation and structured outputs, and check the values against real records.
- 02Error handling in AI applicationsClassify AI failures into four families, catch the SDK’s typed exceptions in the right order, check for successes that are not, and degrade gracefully so customers never see a stack trace.
- 03Batching requests for efficiencyProcess thousands of model requests in minutes with asyncio and a semaphore, or at about half the price with a provider Batch API, and choose between them.
- 04Rate-limit retries and token-limit managementRetry rate-limited calls with exponential backoff and jitter, and keep long conversations inside a token budget by counting, trimming and summarising.
- 05FastAPI basics for AI applicationsSet up a FastAPI project with uv, define routes with path and query parameters, validate requests and responses with Pydantic, and load settings and secrets from the environment.
- 06Building AI endpoints with FastAPIBuild production-style AI endpoints with an async shared client, typed structured responses, exception handlers that map model errors to 502 and 503, and streaming replies.
- 07Quick UIs with Streamlit and GradioBuild a streaming chat UI for internal testing in Streamlit (chat_message, chat_input, session_state) or Gradio (ChatInterface), and know when not to use them.