How do you implement RAG for enterprise knowledge management?

Enterprise RAG connects organizational knowledge across siloed systems into a unified, searchable interface. Data sources: document management (SharePoint, Google Drive, Confluence), communication (Slack, Teams messages and channels), ticketing (Jira, ServiceNow), CRM (Salesforce, HubSpot), code repositories (GitHub, GitLab), and email archives. Connector architecture: build source-specific connectors that handle authentication (OAuth, API keys), pagination, rate limiting, and incremental sync. Use existing connector frameworks (Unstructured, LlamaHub, or custom implementations). Unified schema: normalize metadata across sources (author, createddate, modifieddate, department, project, accesslevel) into a common format stored with each chunk. Access control: critical for enterprise deployment. Map permissions from source systems to metadata filters, enforce at query time, and audit access. Multi-tenancy: support organizational boundaries (departments, teams, projects) with configurable knowledge scopes. Search experience: implement faceted search (filter by source, date, department, author), provide source attribution with direct links to original documents, display confidence indicators, and support follow-up questions. Freshness management: implement crawl schedules per source (more frequent for active sources), track document staleness, and implement priority re-indexing for high-value content. Governance: implement content review workflows for sensitive knowledge, track usage analytics (what questions are asked, which sources are most valuable), identify knowledge gaps (questions with poor retrieval results), and maintain a feedback mechanism for answer quality improvement. Scale: enterprise knowledge bases may contain millions of documents, requiring distributed indexing, efficient storage, and fast retrieval across the corpus.