A developer argues that Large Language Models (LLMs) should not be used for direct numerical computation in financial systems due to their inherent unreliability and lack of auditability. The author proposes an architectural solution where LLMs act as orchestrators, translating user intent into actions for a deterministic Python-based core that handles all calculations. This approach ensures that all financial decisions are based on verifiable, reproducible code, rather than potentially flawed model outputs. The system includes a deterministic scoring core, an enumerated cascade for decision-making, and extensive unit tests to guarantee reliability and auditability, which are crucial for systems handling real money. AI
IMPACT Highlights critical architectural considerations for deploying LLMs in high-stakes quantitative systems, emphasizing reliability and auditability.
RANK_REASON Developer opinion piece on architectural best practices for LLM integration in financial systems.
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