Level: Beginner to Intermediate

This course will take place on Wednesday October 21 and Thursday October 22nd.

Sessions run from 9:00 am - 12:30 pm PT / 12:00 pm - 3:30 pm ET and will be recorded.

Large language models (LLMs) have transformed the way organizations interact with their data through natural language. While these models can deliver impressive results right out of the box, their true business potential is unlocked when they are customized to meet specific needs.

This intensive hands-on workshop equips you with the practical skills to implement retrieval-augmented generation (RAG) systems, empowering your LLMs to access, interpret, and leverage your organization’s unique data assets.

Through a series of lectures, demos, and hands-on activities, you will learn to build and optimize a complete RAG pipeline—from selecting the right models and preparing your data to applying effective retrieval strategies and evaluation techniques, while also implementing robust guardrails for responsible AI use. Through interactive labs and real-world case studies, you will explore both foundational concepts and advanced optimizations that make LLMs indispensable in business environments.

By the end of the workshop, you’ll gain hands-on experience with the essential components needed to develop, evaluate, and deploy customized LLM applications that generate measurable business value.

You Will Learn

• How LLMs function and the practical considerations in model selection
• The fundamentals of prompt engineering to enhance LLM performance
• How to build a complete retrieval-augmented generation (RAG) pipeline
• Techniques for evaluating and measuring RAG system performance
• Methods for identifying and resolving common RAG failure points
• How to implement tracing and guardrails for secure, responsible AI systems
• Best practices for scoping and executing RAG projects that deliver business value

Geared To

• Data scientists and engineers deploying LLM applications
• AI/ML practitioners seeking to enhance their RAG implementation skills
• Software developers integrating LLMs into business solutions
• Technical leaders planning LLM implementation strategies
• Business analysts exploring practical applications of generative AI
• Anyone ready to move beyond basic LLM usage to create tailored, business-specific AI solutions

Pre-requisites

Workshop exercises will feature pre-written Python code, meant to be run in an online notebook.

The instructor will guide you on how to run the code, but comfort reading code will help.

Laptop Setup:

You must have a computer with the required software installed before the bootcamp.

Note on Corporate Laptops:

This course requires browser access to Google Colab and other web services, as well as the ability to download data files and library files.

If your corporate laptop blocks such activities, we recommend you use a personal device instead.

Machine Requirements:

Windows PC or Mac with:

• Google Chrome or another web browser
• Access to Google Drive

Setup:

Laptop preparation is required BEFORE the workshop. Instructions will be available on the seminar site prior to the first session.

There is no time allotted in class for laptop preparation.