Agentic AI Application Development with LLMs

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:

• 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.
• 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 LLM-based agents as well as the key components and the way of working of 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. Also learn about the main building blocks and operation of deep agents based on Claude Agent SDK, OpenAI Codex SDK or Langchain Deepagents SDK.
• 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

• Introduction to LLM based applications
• Main parts, working and training of LLMs
• Using closed- and open-source LLMs via APIs
• LLM app. development frameworks, LangChain
• Prompt engineering
• Retrieval Augmented Generation (RAG)
• Simple Autonomous (ReAct) Agents
• Agentic Workflows, Multi-agent systems, Deep Agents
• Observing (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 or other programming skills, user experience with ChatGPT or similar chatbots.