Production AI Engineering
Getting an AI prototype working is easy. Shipping it to production without it costing a fortune, hallucinating on edge cases, timing out under load, or breaking silently — that is the hard part. This course covers the engineering discipline of production AI: how to evaluate model outputs systematically, control cost and latency at scale, handle failures gracefully, and observe what your AI is actually doing in production.
What you'll learn
Course outline
Free — no account needed
Evaluating LLM Outputs
Build an eval suite before you ship — LLM-as-judge, golden datasets, and regression testing
Token Cost Management
Control your AI spend without degrading quality — caching, compression, and smart model routing
Latency and Streaming
Make AI responses feel instant — streaming architecture, time-to-first-token, and perceived performance
Full course — $89 one-time
Structured Outputs
Get reliable JSON from LLMs every time — tool use, Zod parsing, and retry logic
Handling Failures and Fallbacks
Retry logic, model fallbacks, graceful degradation, and timeout handling for production AI
Observability for AI
Log prompts, track costs, catch regressions, and know what your AI is actually doing in production
Rate Limits and Queuing
Handle Anthropic tier limits gracefully — queuing, prioritisation, and scaling your token budget
Prompt Caching — Deep Dive
Cache system prompts, large documents, and conversation history to cut costs 80%+ at scale
The Production AI Checklist
Everything you need to verify before shipping an AI feature to real users
Get the full course
9 lessons — from eval suites and cost control to observability, rate limiting, and the full production AI checklist.