A hands-on lab where learners configure LiteLLM as a local reverse proxy that intelligently routes requests between multiple LLM backends — a general model for simple prompts, a coding model and a reasoning model for complex ones — instead of hard-coding a single model into an application.

Learn to deploy the Nous Research Hermes Agent, backed by NVIDIA Nemotron-3-Super-120B-A12B, as a persistent infrastructure co-pilot. This hands-on lab guides you through configuring persistent memory across multiple surfaces (the terminal, Mattermost, and a desktop app), enforcing custom engineering standards, and compiling repeatable infrastructure workflows into reusable skills.

The recruitment of patients for clinical trials presents significant challenges for pharmaceutical companies, often delaying drug development, increasing cost, and lead to study failure. This lab explores how AI applied to anonymized health records enables efficient trial matching—at single, bulk, and multi-trial levels—reducing time, improving accuracy, and enhancing access for researchers, providers, and patients.

This lab will help users strengthen their prompt engineering skills using the prompt blueprint framework, which is a structured approach for writing effective prompts. Participants will refine prompts step by step and experiment with blueprint elements to achieve better model outputs.

This lab will walk the lab user through the basics of Generative AI

Explore the NetApp AIPod Mini integrated with Intel® AI for Enterprise RAG. This lab automates the deployment of a secure, scalable ChatQ&A pipeline on Kubernetes. Leverage Intel® Xeon® and Gaudi® accelerators to transform enterprise data into insights, featuring one-click deployment, hardware optimization, and comprehensive observability for production-ready AI workloads.

Primary care and pediatric providers can spend significant time manually cross-referencing multiple sources to construct accelerated catch-up protocols for unvaccinated or under-vaccinated patients. The Pediatric Vaccine Assistant streamlines this workflow, reducing administrative burden and supporting evidence-based care.

This hands-on lab teaches enterprise engineers how to systematically evaluate, benchmark, and select optimal large language models for distinct steps inside an agentic control plane.