Industry insight
Enterprise LLMs and RAG: from knowledge answers to governed workflows
A valuable enterprise LLM is not a chat window; it cites evidence, respects permissions, invokes approved tools and participates in real workflows.
LLMRAGGovernance
Define the task before the model
Start with frequent, measurable and reviewable tasks such as policy Q&A, case routing, first drafts and inspection summaries. Define correctness, citation coverage, human takeover and latency for each task.
The engineering boundary of RAG
Parsing, chunking, metadata, permission filtering, hybrid retrieval, reranking and citation UX all determine quality. A vector database alone cannot solve stale knowledge, conflicting policy or access control.
- Every answer links to inspectable evidence
- Apply user and data permissions before retrieval
- Whitelist and confirm sensitive tool actions
- Continuously evaluate with real business questions
Joining the workflow
Mature systems place models inside auditable steps: draft, extract, call an approved API, await approval, record the result and learn from human feedback.