📄 中文摘要
在大型语言模型（LLM）中，'幻觉'问题是一个关键挑战，指的是模型生成不准确或虚构的回答，尤其是在处理不一致的文本描述中的货币值时。缺乏对金融数据的准确处理能力，LLM可能会提供误导性结果，无法满足商业应用的精确要求。为了解决这一问题，ETL-D环境中的'/v1/enrich/amount'端点被用来提取和清理货币值，从而确保LLM接收到准确的数据。
为人工智能赋能：V1 增强金额 (ETL-D API)
出处: Dotando a IAs con: V1 Enrich Amount (ETL-D API)
发布: 2026年3月30日
📄 English Summary
Dotando a IAs con: V1 Enrich Amount (ETL-D API)
The 'hallucination' problem in large language models (LLMs) presents a significant challenge, characterized by the generation of inaccurate or fabricated responses, particularly when interpreting monetary values from inconsistent textual descriptions. Without the ability to accurately process financial data, LLMs can produce misleading results that fail to meet the precision requirements of commercial applications. To address this issue, the '/v1/enrich/amount' endpoint in the ETL-D environment is utilized to extract and clean monetary values, ensuring that the LLM receives accurate data.