How LLMs Transform Investment Workflows: Fine-Tuning, RAG & Agents with Francesco Fabozzi

In Part 2, Francesco Fabozzi, PhD—Managing Editor of the Journal of Financial Data Science—joins host Lotta Moberg, CFA, PhD, to explore how modern NLP and large language models are reshaping investment management. Building on the technical foundations from Part 1, this episode turns to real-world applications: when to fine‑tune models versus rely on prompt engineering, how retrieval‑augmented generation (RAG) keeps models current with fast‑changing financial information, and why agentic systems are emerging as powerful tools for research automation. Fabozzi explains practical use cases ranging from sentiment‑driven return prediction to efficient knowledge‑distillation workflows, research assistants that read earnings reports, and coding agents that help back‑test investment ideas. The discussion closes with a look at where innovation is headed, including the potential of "general price transformers" for market forecasting. This episode is essential for anyone applying AI within investment processes.