AI-434 GPT and Open Source LLM App Development - Detailed Course Outline

Course Length

32 training hours

Course Overview

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.

Learning Objectives

At the end of the training participants will be able to:

• Recognize the common types of LLM-based applications used today
• Understand the basic building blocks of modern large language models, how they operate, and the 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
• 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, deep agents and multi-agent systems are useful in more complex agentic applications and learn about the most popular agentic frameworks
• Recognize the importance of 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
• Creating LLM chains with LangChain
• Fast Web Interface Prototyping for LLMs
• Prompt engineering for chatbots and agents
• Retrieval Augmented Generation (RAG)
• LLM-based Agentic Systems
• Workflows, Deep Agents, Multi-agent Systems and Agentic Frameworks (optional)
• Tracing and Evaluating LLM-based apps

With the help of these building blocks a team of engineers can develop cutting edge applications like customer service bots, knowledge base managers and research assistants.

Structure

50% theory, 50% hands on lab exercises

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.

Course Modules

PART I. Basic Concepts

Module 1: Introduction to LLM based applications

Main usage areas of LLM-based applications
Main types of LLM-based applications
Building blocks of LLM-based applications

Module 2: Main parts, working and training of LLMs

Main parts and working of LLMs in a nutshell
The 3+1 parts of LLM training
In-context Learning
Most important base LLM vendors and models
Lab: Testing text generation of different GPT model generations

PART II. Application Development with LLMs

Module 3: Using closed- and open-source LLMs via APIs

Using LLMs through APIs
Typical LLM parameters
Jupyter Lab basics
Lab: Using popular closed- and open-source LLMs via the Python APIs

Module 4: App. development frameworks, LangChain

LLM app. development frameworks
Main features of Langchain
Langchain components
LangChain Memory
Lab: Using LangChain together with popular closed- and open-source LLMs

Module 5: Fast Web Interface Prototyping for LLMs (Gradio)

What is Gradio?
Main features of Gradio
Building simple GUIs with the ChatInterface class
Building more complex GUIs with the Block class
Lab: Building simple and more complex GUIs with Gradio

Module 6: Prompt engineering for chatbots and agents

The 4 golden rules of prompt engineering
10 Prompting rules of thumb:
• Be concise and give clear instructions
• Be specific and include relevant details
• Add positive and negative prompts
• Define roles for the LLM
• Define roles for the LLM's audience
• Provide examples for the solution or response style (one-shot or few-shot prompting)
• Add relevant context
• Divide difficult tasks into subtasks (Prompt Chaining)
• "Let's think step by step" (Chain of Thought)
• Let the LLM ask questions
Lab: Prompt engineering tasks

Module 7: Retrieval Augmented Generation (RAG)

What is Retrieval Augmented Generation (RAG)
How do RAG systems basically work?
Implementation details
Lab 1: Creating simple agentic RAG systems
Advanced RAG techniques
New directions in RAG
Lab 2: Creating advanced RAG systems

Module 8: LLM-based Agentic Systems

Motivations for LLM-based Agentic Systems
Main Features of and Difference between LLM Workflows and Agents
Main Building Blocks: Functions, Tools, Agents
The ReAct autonomous agent execution logic
Implementing Functions, Tools and the ReAct agent execution logic with LangChain
Lab: Creating and using simple LangChain autonomous agents

Module 9: Workflows, Deep Agents, Multi-agent systems and Agentic Frameworks

Problems with the ReAct model
First solution: workflows
Third solution: deep agents
Most popular agentic frameworks

Module 10: Tracing and Evaluating LLM-based apps (LangSmith)

Why do we need them during development?
Debugging LangChain-based programs without any monitoring software
Debugging and evaluation tools for LLM-based apps
Introducing and Initializing LangSmith
LangSmith tracing primitives
Tracing: using LangSmith without and with LangChain
Lab: LangSmith Tracing
Introduction to LangSmith Evaluation
Examples of different types of evals
LangSmith evaluation primitives
Main steps of LangSmith evaluation
Lab: LangSmith Evaluation

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AI-434

AI Application Development with LLMs