Unverified50% confidenceSolutionExact time
The best practice for financial applications is to have LLMs responsible only for parsing user intent and explaining formula logic, while delegating actual numerical computation to deterministic code (such as Python's decimal library).
1
Sources
50%
Confidence
Long-term
Relevance
10/11/2026
First Seen
Sources
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Related Claims
UnverifiedAnythingLLM 本身不直接运行大模型推理计算，而是作为编排层负责用户界面、文档处理、向量检索、模型调度等上层逻辑71% similarUnverified对于语义解析和意图路由需求，应让LLM负责语义映射，而将几何计算交给专业库执行，不应让LLM直接做精确浮点运算70% similarUnverifiedIn traditional quantitative trading systems, strategy logic is typically hardcoded in the form of code (such as Python or Pine Script) and does not rely on real-time inference from external models at runtime.67% similarUnverifiedLarge language models have limitations in precise numerical reasoning, so actual products often adopt a hybrid architecture in which the LLM understands intent and generates candidate solutions while a structured engine validates constraints.67% similarUnverifiedFor operations involving funds, one should assume the model will inevitably be manipulated, and any financial operation must pass through a deterministic validation layer independent of LLM inference.66% similar
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