Building Production LLM Systems

The course covers RAG architecture and retrieval system design, embedding models and vector databases, structured output and validation pipelines, agent patterns and production tool use, prompt caching and cost optimisation, LLM evaluation frameworks (including LLM-as-judge and RAGAS), production observability with LangSmith and Braintrust, multimodal integration, and fine-tuning and model selection decisions.

AI for Software Engineers is the recommended prerequisite. This course assumes you have already shipped at least one LLM-powered feature and understand the basic integration patterns. If you are new to LLM integration, start with AI for Software Engineers first.

Yes, where relevant. The course covers LangGraph for stateful agent graphs, and discusses when agent frameworks add value vs when raw API integration is simpler and more reliable. The emphasis is on patterns that transfer across frameworks rather than any single library.

Yes, completely. There is no subscription, no credit card, and no paywall. Every lesson, every exam attempt, and every certificate is free.

Most courses take 2–5 hours of lesson reading, spread across a few sessions — check the estimated time above for this specific course. You can read at your own pace, with no time pressure until you start the exam.

After completing all lessons you unlock the exam. It is multiple choice, server-graded, with a time limit. You need 75% to pass. You have 3 attempts included.

Yes. You have 3 attempts total. After each failed attempt you can review the course before retrying. After all attempts are used, correct answers and explanations are revealed.

Every certificate has a unique ID and a public verification URL that anyone can check without an account. Employers, clients, and recruiters can verify it independently at any time.

Yes. Your certificate page includes a direct LinkedIn "Add to Profile" link that pre-fills the certification details. It takes one click.