In recent years, more and more enterprises have integrated large models into their internal knowledge bases and built question and answer assistants using Retrieval Enhanced Generation (RAG). However, many projects have achieved stunning results during the demonstration phase, and as soon as they go live, they immediately answer irrelevant questions: citing outdated documents, unable to come up with new policies, and conflicting statements from different departments. The root cause is often not in the model, but in the knowledge base itself.
1、 A knowledge base is not something that can be used simply by throwing it in
The sources of enterprise documents are diverse: institutional documents, meeting minutes, product manuals, chat records, and third-party materials. If stored directly without cleaning, duplicate, conflicting, and expired content will interfere with the search results, making it difficult for the model to determine which one to trust.
2、 Four common types of problems
1. Outdated content: The old version of the system has not been taken down, and the search results show expired clauses;
2. Conflicting statements: Multiple departments have uploaded inconsistent operational standards;
3. Unrestricted permissions: Confidential content and public content are mixed, and unauthorized disclosure may occur during Q&A sessions;
4. Chaotic structure: Long documents are not split and annotated, resulting in incomplete semantic retrieval of fragments.
3、 The Four Actions of Knowledge Base Governance
1. Clean and remove duplicates before storage, clarify the document responsible person and expiration date;
2. Establish a 'unique source of facts', with institutional content based on the published version, and old versions archived and isolated;
3. Partition by permission and confidentiality level, and overlay permission filtering during retrieval;
4. Do a good job of partitioning and metadata (title, department, date, scope of application) to make the search more accurate.
4、 Continuous operation is more important than one-time construction
The knowledge base needs to operate like a product: regularly inspecting expired content, collecting "wrong answer" cases for feedback and optimization, and establishing a collaborative mechanism between business and IT. The upper limit of RAG is determined by the quality of the knowledge base - only by governing the knowledge base well can the accuracy of Q&A and user trust be synchronously improved.
【 Reference source 】 Comprehensive compilation of industry information and practical experience released publicly.