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Module · 5 lessons
Shipping AI Applications
Take an LLM prototype to production — handle failures with retries and streaming, control cost and tokens, secure your keys, and serve your app behind a real API.
At a glance
Level
Advanced
Lessons
5 lessons
Time to complete
1 week
Cost
Free forever · no sign-up
Welcome to Shipping AI Applications, the tenth and final module of the Generative AI & LLM Engineering course. Across this course you’ve built embeddings search, RAG, agents, and framework-based systems — all as prototypes that run on your machine. This module is about the gap between works on my laptop and something other people can depend on. Production code has to survive failures, stay within a budget, keep secrets safe, and answer over a network — and none of that is optional once real users show up.
You’ll start with what actually changes when you ship, then make your calls reliable with streaming and proper error handling (retries, timeouts, rate limits). You’ll learn to manage cost and tokens — counting tokens, tracking usage, caching, and picking the right model — so your bill doesn’t surprise you. You’ll secure and validate your application, keeping API keys out of your code and rejecting bad input. Finally you’ll serve your app behind a real API with FastAPI, and the capstone packages everything into a deployable AI service.
Every example runs for real against the Claude API on the affordable claude-haiku-4-5 model, including a working FastAPI endpoint tested end to end. By the end you’ll be able to take any of the systems you built in this course and turn it into a service that’s reliable, affordable, secure, and ready to deploy — the final skill that separates an LLM hobbyist from an LLM engineer.
Start with Lesson 1, where you’ll learn exactly what changes on the road from prototype to production.
Lessons in this module
1
From Prototype to Production
Understand what changes when you ship an LLM app — reliability, cost, security, serving, and observability — and see the usage data that drives production decisions.
2
Streaming and Error Handling
Make LLM calls reliable: stream long responses to cut perceived latency and avoid timeouts, and handle rate limits, timeouts, and transient errors with retries.
3
Managing Cost and Tokens
Control LLM spend: read token usage and turn it into dollars, count tokens before sending, pick the right model, cap output, and cache reused prompts.
4
Securing and Serving
Secure your AI app and serve it over HTTP: keep API keys out of code, validate untrusted input, and expose your model through a FastAPI endpoint with schemas and error handling.
5
Guided Project: Deploying an AI App
Package everything into one deployable FastAPI service: retries, cost tracking, env-based secrets, validated schemas, health checks, and request logging — ready to run.
Achievement
Complete all 5 lessons to finish the Shipping AI Applications module.