As the financial services industry embraces GenAI to drive innovation and efficiency, the need for robust and trustworthy outcomes becomes paramount. Traditional RAG pipelines, while powerful, often fall short in addressing the unique challenges of the finance domain, such as data and model interpretability. DSPy emerges as a game-changer, offering a comprehensive solution tailored to the intricate requirements of domain-specific use cases. By seamlessly integrating LLMs with structured programming constructs, DSPy empowers developers to build GenAI applications that are not only highly performant but also more predictable, auditable, and compliant. This talk delves into the architectural advantages of DSPy over traditional RAG pipelines, highlighting its ability to optimise prompt instructions, enable fine-grained optimisation, and facilitate interpretable decision-making processes. Through real-world use cases and code examples, we will explore how DSPy's unique features, such as signature-based programming, chained LLM calls, and optimisation techniques, can unlock new frontiers in GenAI adoption across Financial Services. Attendees will gain insights into the transformative potential of DSPy in optimising GenAI outcomes, ensuring regulatory adherence, and fostering trust in AI-driven Financial Services.