Large Language Models (LLM) Intro

Course Description

The fast pace of development in LLMs and related technologies, such as agentic AI, 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 software 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 applications and understand their main building blocks.
• Get a high-level understanding of how modern large language models (LLMs) work and how they are 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.
• Understand the main ideas behind prompt engineering, including 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 and the two main types of LLM-based agentic systems (workflows and autonomous agents) as well as the key components and the way of working of autonomous agents.
• Recognize the importance and services of monitoring and evaluating LLM applications throughout their lifecycle as well as learning about some leading tools in this field (optional).

Main topics:

• Introduction to LLM based applications: current types, building blocks, challenges
• Why and how LLMs work and are trained?
• 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 (optional)

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 and play with them during instructor's demonstrations and lab exercises.

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.