Intro to Large Language Models (LLMs) and Agentic AI apps

Course Description

The fast pace of development in LLMs and related technologies made it possible to use them even in enterprise grade applications. There are already a few areas where a new generation of LLM-based applications totally redefined applications' capabilities and users' expectations, while AI technologies are going to radically change all kinds of other technology areas as well.

That's why IT and other technical managers and professionals need to understand the technologies used in AI applications such as LLMs, RAG and agents.

Training objectives:

At the end of the training participants:

• Recognize common LLM-based application types and understand their main building blocks.
• Get a high-level understanding of how modern large language models (LLMs) work and how they have been trained in multiple steps.
• Explain the pros and cons of using LLMs via their APIs and via frameworks and be familiar with some popular open-source options for both.
• Understand the main ideas behind prompt engineering as well as practical tips and best practices for working effectively with modern LLMs.
• Know the basics of RAG (Retrieval-Augmented Generation) systems, including their main parts, ways to improve their performance, and some new alternative solutions.
• Understand the motivations for LLM-based agents as well as the key components and the way of working of simple autonomous (ReAct) agents.
• Learn why workflows, multi-agent systems and deep agents (a.k.a. advanced autonomous agents) are useful in more complex agentic applications.
• Recognize the importance of observing (tracing) and evaluating LLM-based applications throughout their lifecycle.

Main topics:

• Introduction to LLM-based applications: current types, building blocks, challenges
• Why and how LLMs work and have been trained before using them
• Using closed- and open-source LLMs via APIs and app. development frameworks
• Prompt engineering
• "Talk with your documents": Retrieval Augmented Generation (RAG)
• "AI that thinks and acts": LLM Agents
• Quality Assurance at LLM apps: Tracing and Evaluation

Besides gaining a basic understanding of Large Language Models (LLMs) and other technologies used in LLM-based applications, students will be able to examine their features during the instructor's demonstrations.

Target Audience

Technical managers and professionals who want to familiarize themselves with Large Language Models (LLMs) and LLM-based applications.

Prerequisites

Basic understanding of IT concepts, User experience with ChatGPT or similar chatbots.

Structure

50% lecture, 50% demonstration by the instructor. Students can do hands-on lab exercises outside of training hours.