Take LLM systems from prototype to dependable production. This track covers prompt and context engineering, RAG pipelines, evaluation, monitoring and guardrails — the operational discipline of running AI at scale.
The full lifecycle of a production LLM system, hands-on.
Getting an LLM demo working is easy; running one reliably, securely and affordably in production is not. LLMOps is the engineering discipline that closes that gap.
This track equips AI and platform engineers to build retrieval pipelines, evaluate quality objectively, control cost and latency, and operate LLM systems with the same rigor as any other production service.
Prompt and context engineering for reliable, repeatable behavior
RAG pipelines — chunking, embeddings, retrieval quality and grounding
Evaluation harnesses and offline/online testing for LLM output
Monitoring, observability, cost and latency management at scale
Security and safety guardrails — injection defense, PII handling, output filtering
The Quantum Clock Is Ticking
Security experts estimate quantum computers capable of breaking RSA-2048 encryption could arrive by 2030-2035. Adversaries are already running "Harvest Now, Decrypt Later" campaigns. Upskilling your teams now is the difference between leading the transition and scrambling to catch up after the deadline.
Outcomes that turn promising prototypes into production-grade systems.
A repeatable path from LLM prototype to reliable production deployment
RAG and context patterns that improve accuracy and reduce hallucination
Objective evaluation so quality is measured, not guessed
Operational control over cost, latency and reliability
Built-in safety and security guardrails for LLM applications