Unverified50% confidenceOpinionExact time
LLM可能引入训练数据中存在的反模式、过时的API用法甚至已知的安全漏洞模式如SQL注入、路径遍历
1
Sources
50%
Confidence
Long-term
Relevance
8/23/2026
First Seen
Sources
Related Entities
Related Claims
Unverified通用大模型的训练数据存在滞后,对最新框架特性掌握不足,容易生成已被废弃的System.Web写法或错误迁移.NET Framework API71% similarVerifiedThe RAG mechanism retrieves semantically similar business data fragments from a vector database and injects them into LLM prompts, addressing LLM knowledge cutoff and hallucination issues without requiring model retraining69% similarUnverifiedBecause LLM training data typically comes from large-scale web scraping and many benchmark questions are publicly available online, benchmark contamination is an almost unavoidable systemic problem69% similarUnverified研究表明LLM生成的代码在SQL注入、XSS防护、密钥硬编码等方面的错误率显著高于有经验的人类开发者68% similarUnverifiedRAG technology can effectively address the 'hallucination' problem of LLMs, because LLMs have a training data cutoff date and cannot directly access enterprise private data.68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/790012API
curl https://kongchang.com/api/v1/knowledge/claims/790012MCP
get_claim(id=790012)