The fastest path to a production-grade AI API. FastAPI + Python + Claude. Build REST endpoints, add AI capabilities, handle auth and rate limiting, document automatically, and deploy — all in 5 days.
This is a text-first course that links out to the best supporting material on the internet instead of trying to replace it. The goal is to make this the best course on api development you can find — even without producing a single minute of custom video.
FastAPI generates OpenAPI docs automatically, handles async natively, and is the most common choice for AI service APIs. This course explains why.
This isn't a generic REST API course — it covers the specific patterns that AI APIs require: streaming responses, long timeouts, prompt injection prevention.
The course builds one API across 5 days. By the last session you have a live endpoint you can share and use.
Each day is designed to finish in about an hour of focused reading plus hands-on coding. No live classes, no quizzes.
Each day stands alone. Read them in order for the full picture, or jump straight to the day that answers the question you have today.
REST API fundamentals, HTTP verbs, status codes. Why AI capabilities are delivered as APIs. The architecture pattern that every AI product uses.
FastAPI setup, path operations, request/response models with Pydantic, automatic docs generation. Your first working API endpoint.
Wrapping Claude API calls in FastAPI endpoints. Streaming responses. Structured output with JSON schemas. Prompt templates as API inputs.
API key auth, JWT tokens, rate limiting with Redis, error handling that doesn't leak system information. Making your API production-safe.
Deploying FastAPI to Railway or Fly.io, setting environment variables, generating and hosting API documentation, monitoring with logs.
Instead of shooting our own videos, we link to the best deep-dives already on YouTube. Watch them alongside the course. All external, all free, all from builders who ship this stuff.
Complete FastAPI walkthroughs — from installation to building production-grade REST APIs with Python.
Building AI-powered endpoints with FastAPI and Claude — streaming responses, structured output, and prompt management.
Best practices for API design — naming, versioning, error handling, and the patterns that make APIs easy to use.
JWT authentication, API key management, and OAuth patterns in FastAPI — the security layer every production API needs.
Deploying FastAPI applications to Railway, Render, and Fly.io — from local to live in under 30 minutes.
Pydantic for data validation and serialization — the library that makes FastAPI's request/response models work.
The best way to go deeper on any topic is to read canonical open-source implementations. These repositories implement the core patterns covered in this course.
FastAPI itself — the most important repo to understand for this course. Well-documented, with excellent examples in the source.
The data validation library that powers FastAPI's models. Understanding Pydantic is essential for building reliable API inputs and outputs.
The official Python SDK for the Anthropic Claude API — the AI backend for the endpoints you build in this course.
The ASGI framework that FastAPI is built on. Reading Starlette's source helps you understand what FastAPI does under the hood.
You build APIs and want to add AI capabilities. This course shows you the exact patterns for wrapping AI models in production-grade REST endpoints.
You know Python and want to ship AI capabilities as APIs. FastAPI is the fastest path — this course gets you there in 5 days.
You are building a product that needs AI capabilities exposed as an API. This course gives you the backend foundation to build on.
The 2-day in-person Precision AI Academy bootcamp covers AI API development and deployment — hands-on with Bo. 5 U.S. cities. $1,490. 40 seats max. June–October 2026 (Thu–Fri).
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