AI-434: GPT and Open-source LLM Application Developer
Course description: Artificial intelligence has become an extremely important area for IT professionals and engineers in the past 10-20 years with the scientific breakthroughs and practical applications of deep learning and more recently of generative AI systems, especially with its Large Language Model (LLM) variant such as OpenAI’s ChatGPT and Google’s Bard and many open-source models. Due to its importance and impact on every aspect of our lives, understanding the concepts, functionalities and practical usage of AI systems is quickly becoming essential for all IT and other technical professionals as well as for managers with technical background.
This training focuses on LLM concepts as well as GPT and Open-source LLM prompt engineering and application development, and teaches participants the following topics:
- Introduction to LLM based applications
- The Foundations: Neural Networks, Deep Learning, CNN, RNN, Transfer Learning
- “Attention is all you need” – The Transformer Architecture
- The 3-phase training process of LLMs (pre-training, fine-tuning, RLHF)
- Basics of prompt engineering
- Advanced prompt engineering techniques
- Retriever Augmented Generation (RAG)
- Creating LLM chains with LangChain
- Fast Web Interface Prototyping for LLMs (Gradio and Streamlit)
- Debugging and Evaluating LLM-based apps (Langsmith, Langfuse and TrueLens)
- Fine-tuning Open-source LLM models
- Basics of AI Threats and LLM Security (optional)
Besides gaining a basic understanding of the concepts of prompt engineering, students will also do extensive lab exercises using the Python APIs of the GPT as well as popular and powerful Open-source LLMs to see how these concepts work in practice.
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 concepts, techniques, and best practices of ChatGPT/GPT prompt engineering and application development with the GPT Python API as well as the Python APIs of popular and powerful Open-source LLMs.
Prerequisites: Basic understanding of AI, Machine Learning and Deep Learning concepts. Basic Python programming skills. Some experience in using the Python based PyTorch deep-learning frameworks is an advantage.
Suggested preliminary course: nice to have, but not necessary: AI-212 Deep Learning Foundations with PyTorch