Structural and Programmatic Prompting in Financial Artificial Intelligence
Abstract
The intersection of artificial intelligence and quantitative finance is under fundamental
paradigm shift. This paper explores the transition from natural language prompting to
structured, formulaic, and programmatic methodologies. By evaluating advanced architectures such as Program of Thoughts (PoT) and Formula-One (F-1) prompting, as well as neurosymbolic integration, we demonstrate how bridging the gap between probabilistic text
generation and deterministic mathematical logic unlocks unprecedented accuracy in financial
reasoning.
paradigm shift. This paper explores the transition from natural language prompting to
structured, formulaic, and programmatic methodologies. By evaluating advanced architectures such as Program of Thoughts (PoT) and Formula-One (F-1) prompting, as well as neurosymbolic integration, we demonstrate how bridging the gap between probabilistic text
generation and deterministic mathematical logic unlocks unprecedented accuracy in financial
reasoning.
