Agentic AI Application Development with LLMs (Extended Version)

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 software areas as well.

That's why software developers as well as other IT professionals and technical managers need to understand these technologies, especially agentic AI, and need to have practical skills to use them in their daily work.

Training Objectives

At the end of the training participants will:

• Understand the basic building blocks of modern large language models, how they operate, and their multi-step training process
• Write simple programs using both open- and closed-source LLMs, either through their own APIs or with popular frameworks like LangChain
• Create simple GUIs for LLM-based applications in Python or JavaScript
• Understand the main ideas behind prompt engineering, including practical tips and best practices for working effectively with modern LLMs in chatbots and agentic applications
• Grasp the fundamental ideas behind RAG (Retrieval-Augmented Generation) systems and apply both its basic and more advanced versions in LLM-based agents
• 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
• Learn why workflows, multi-agent systems and deep agents are useful in more complex agentic applications and learn about the most popular agentic frameworks
• Recognize the importance of observing (tracing) and evaluating LLM-based applications throughout their lifecycle and get hands-on experience with tools like LangSmith for tracing and evaluation

Main Topics

• Main parts, working and training of LLMs
• Using closed- and open-source LLMs via APIs
• Creating LLM chains with LangChain
• Fast Web Interface Prototyping for LLMs
• Prompt engineering
• Retrieval Augmented Generation (RAG)
• LLM-based Agentic Systems
• Workflows, Multi-agent Systems and Agentic Frameworks
• Tracing and Evaluating LLM-based apps

Target Audience

Software developers, testers, DevOps as well as other IT professionals and technical managers with technical backgrounds who want to understand the basic concepts and technologies behind Large Language Models (LLMs) and want to obtain practical skills in LLM application development with the Python APIs of popular closed- and open-source LLMs and open-source frameworks.

Prerequisites

Basic understanding of AI concepts, basic Python programming skills, user experience with ChatGPT or similar chatbots.