AI-434: GenAI Application Development with LLMs
(OpenAI GPT, Google Gemini, Anthropic Claude, Meta Llama, Mistral, DeepSeek)
Course description: Artificial intelligence has become an extremely important area for IT professionals and engineers with the scientific breakthroughs and practical applications of generative AI systems, especially its Large Language Model (LLM) variant such as OpenAI’s GPT, Google’s Gemini and many other closed- and open-source models. Due to its importance and impact on every aspect of our lives, understanding the concepts, functionalities as well as their usage in LLM-based applications is quickly becoming essential for all IT and other technical professionals as well as for managers with technical background.
This training focuses on LLM related concepts and building blocks as well as prompt engineering and application development, and teaches participants the following topics:
- Introduction to LLM based applications
- Main parts and working of LLMs (Tokenizer, Embedding, Transformer, etc.)
- The 3-phase training process of LLMs (pre-training, fine-tuning, RLHF/DPO)
- Using closed- and open-source LLMs via APIs
- Prompt engineering
- Retriever Augmented Generation (RAG)
- Creating LLM chains with LangChain
- LLM Agents
- Multi-agent systems and Agent Frameworks (LangGraph, AutoGen, CrewAI)
- Fast Web Interface Prototyping for LLMs (Gradio)
- Debugging and Evaluating LLM-based apps (Langsmith)
- Fine-tuning Open-source LLM models
Besides learning about LLM concepts, students will also do extensive lab exercises using the Python APIs of popular closed-source OpenAI GPT, Google Gemini, Anthropic Claude as well as open-source Meta’s Llama and Mistral models LLMs to see how these concepts work in practice. During the exercises they use LangChain products such as LangChain, LangGraph and LangSmith, to implement LLM concepts in real world LLM applications.
Course Length: 32 training hours
Structure: 50% theory, 50% hands on lab exercises
Target audience: Software developers and other IT and technical professionals as well as managers with technical background 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.
Prerequisites: Basic understanding of AI concepts, basic Python programming skills, user experience with ChatGPT or similar chatbots.