Welcome to LLMs in Production

Your LLM-powered demo works perfectly. You ship it to 10,000 users. Everything breaks. Latency is fine in the demo because you tested with 5 prompts. In production, you hit rate limits at 9am. Cost was a rounding error in dev. In prod, one user with a bad prompt costs you 50 dollars in a minute. Quality looked great when you eyeballed the outputs. With real users, you find out the model hallucinates citations, ignores instructions on long prompts, and gives different answers to the same question. This is the gap this course exists to close. Going from demo to production is not just about "adding observability." It's a different discipline: evals, latency budgets, RAG architectures, cost attribution, prompt versioning, drift detection. The teams shipping LLMs at scale have built a real engineering practice around this. You can learn it. We'll cover: • Demo vs production: the 5 things that always break • LLM monitoring fundamentals: quality, cost, latency, drift • Tracking cost and latency in practice (with examples) • Setting up production monitoring: logging, evals, SLOs, alerts • RAG: how retrieval-augmented generation works and when to use it • Production-grade RAG: chunking, embeddings, vector DBs, reranking • RAG failure modes and how to fix them • Comparing tools: LangSmith, Langfuse, Helicone, Arize • Agent observability: tracing multi-step workflows • Cost-effectiveness benchmarking This course assumes you've shipped some code before and have basic familiarity with LLMs (ChatGPT or Claude). You don't need to be an ML engineer. Time: ~3 to 4 hours across 10 articles.