LLMs in Finance Masterclass

Large Language Models (LLMs) have become the backbone of modern AI systems, powering applications from natural language understanding to complex, multi-agent systems. This masterclass explores advanced techniques for designing, optimizing, and deploying LLM-based systems, with a particular focus on agentic architectures, retrieval augmented generation (RAG), advanced reasoning strategies, and hands-on implementations. You will gain practical knowledge of cutting-edge tools and frameworks, including LangGraph and LangGraph Studio, and will work with Jupyter notebooks provided for live coding demonstrations and experimentation.

• Understand the architecture and core components of transformer-based LLMs, including tokenization, embeddings, and attention mechanisms.
• Apply advanced model optimization techniques such as PEFT(LoRA, adapters), quantization, and sharding to improve model efficiency and deployability.
• Design and implement Retrieval-Augmented Generation(RAG) systems, optimize chunking strategies, and troubleshoot performance issues specific to financial use cases.

This course is designed for quants, analysts and data scientists, and IT professionals seeking to understand how to train, optimize and build advanced LLMs and agent-based systems powered by LLMs. Participants will learn how to design, implement, and optimize intelligent agents for real-world applications, leveraging state-of-the-art tools and frameworks.