Agents powered by large language models (LLMs) are quickly gaining popularity from both individuals and companies as people are finding new emerging capabilities and opportunities to greatly improve their productivity. An especially powerful recent development has been the popularization of retrieval-based LLM systems that can hold informed conversations by using tools, looking at documents, and planning their approaches. These systems are very fun to experiment with and offer unprecedented opportunities to make life easier, but also require many queries to large deep learning models and need to be implemented efficiently.
You will be designing retrieval-augmented generation systems and bundling them into deliverable formats. Along the way, you will learn advanced LLM composition techniques for internal reasoning, dialog management, and tooling.
Learning Objectives
By participating in this workshop, you’ll learn how to:
- Compose an LLM system that can interact predictably with a user by leveraging internal and external reasoning components.
- Design a dialog management and document reasoning system that maintains state and coerces information into structured formats.
- Leverage embedding models for efficient similarity queries for content retrieval and dialog guardrailing.
- Implement, modularize, and evaluate a retrieval-augmented generation agent that can answer questions about the research papers in its dataset without any fine-tuning.
Download workshop datasheet (PDF 106 KB)