The demand for tools to simplify and optimize generative AI development is skyrocketing. Applications based on retrieval-augmented generation (RAG) — a technique for enhancing the accuracy and reliability of generative AI models with facts fetched from specified external sources — and customized models are enabling developers to tune AI models to their specific needs. How and why do financial leaders care about this new technique? Attend this breakout to learn why.