Often-Ignored Data Governance Issues in Enterprise AI Application Development

On November 10, 2026, an article titled "企业级 AI 应用开发中容易忽视的数据治理问题" appeared on DEV Community, in its AI section.

Drawing on experience building AI applications and RAG architectures, the author observes that the industry spends a great deal of time discussing LLMs, embeddings, vector databases, agents, and prompts, while the data itself receives far less attention — a gap that can become a genuine problem.

The article works through a simple example to make the point concrete. Suppose you build an AI assistant for an enterprise, connect it to company documentation, and ask: "What is our vendor onboarding process?" The knowledge base returns several conflicting sources: an old vendor onboarding document, a dated HR policy file, and a flowchart that does not match the process the procurement team actually follows today.

From this, the article concludes that in enterprise RAG and LLM application development, messy, duplicated, and uncurated documents sitting at the base of the stack are the core hidden risk behind hallucinated outputs — more so than the model or the vector database that sit on top of them.

As of the material available, this is the only report on the issue. The discussion ends here, with no follow-up developments recorded.