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.
Written by the RadarTrek editorial team · Reviewed June 2026
About this course
Building a demo AI application is straightforward. Building one that works reliably in production — handling failures, managing costs, maintaining quality as usage scales, and monitoring for regressions — is a different engineering challenge. This production AI engineering course covers the patterns that separate toy demos from commercial AI products: evals, observability, error handling, fallback strategies, prompt versioning, cost monitoring, and latency optimisation.
This course is for developers who have already built AI features and want to make them production-grade, and for engineering leads establishing AI development best practices for their teams. After completing it you will have a framework for deploying AI features confidently — with quality gates, alerting, cost dashboards, and the discipline to catch regressions before users do.
Frequently asked questions
What is an AI eval and why do I need one?
An eval is a test that measures the quality of an AI model output — whether it answers correctly, stays in format, avoids prohibited content, or achieves a specific goal. Unlike traditional unit tests with deterministic pass/fail outcomes, AI evals often involve scoring or human review. Running evals before deploying prompt changes is the equivalent of running a test suite before deploying code — it prevents quality regressions.
How do I monitor an AI application in production?
AI monitoring involves tracking: latency (how long each AI call takes), cost (tokens consumed per request and per user), error rate (API failures, timeouts, rate limit hits), and quality metrics (user feedback, task completion, hallucination detection). Tools like Langsmith, Langfuse, and Helicone provide AI-specific observability. This course covers setting up monitoring that alerts you when something goes wrong.
How should I handle AI API failures?
AI APIs fail in ways different from typical services — they timeout under load, return 429 rate limit errors during traffic spikes, and occasionally produce malformed responses. Robust AI applications implement: exponential backoff and retry logic, fallback to a smaller or alternative model, graceful degradation, and circuit breakers that stop hammering a failing API. This course covers implementing each pattern.
How do I version and manage prompts?
Prompts are software artifacts that need version control, review, and deployment processes. At minimum, prompts should be in version-controlled files (not hardcoded strings in application code), with a staging environment to test changes before production. Production systems often use a prompt management tool (Langsmith, PromptLayer) that provides version history, A/B testing, and rollback capability.
What is latency optimisation for AI applications?
AI latency is measured in seconds, not milliseconds, and has a large impact on UX. Optimisation techniques include: streaming (display tokens as they arrive), parallelising independent AI calls, caching responses for identical inputs, choosing smaller models for simpler tasks, and pre-generating content during off-peak hours. This course covers measuring AI latency and applying optimisations with the highest impact-to-effort ratio.