RAG · May 6, 2026 · 8 min read
RAG, grounded: enterprise GenAI that doesn't hallucinate
Public chatbots guess. Grounded, retrieval-augmented GenAI cites your own data — here's the architecture that makes it safe and accurate.
The difference between a demo and a deployable enterprise assistant is grounding. Without it, an LLM confidently invents answers and can leak data. With it, every answer is traceable to a source you own.
The grounded pipeline
- Ingest & chunk your knowledge; embed into a vector store.
- Retrieve the most relevant passages for each query.
- Generate an answer constrained to those passages — with citations.
- Evaluate continuously and guardrail the edges.
Make it measurable
Wrap the system in an evaluation harness: groundedness, answer accuracy, and refusal behaviour. Target 90%+ grounded accuracy before you widen access, and keep the eval running in production.
Citations aren't decoration — they're the audit trail that makes GenAI trustworthy at work.
Deploy privately so your data never trains someone else's model, and you get the productivity without the risk.