For Heads-of · Practitioner
Retrieval-Augmented Generation (RAG)
A technique where an LLM retrieves relevant documents or data before generating a response, grounding outputs in actual information rather than relying on training data alone.
- RAG
- retrieval augmented generation
- grounding
- fact checking
How It Works
The system retrieves semantically similar documents using embeddings, then passes them to the LLM as context. The model generates responses informed by the retrieved information.
Benefits
RAG reduces hallucinations by providing factual grounding, enables use of private or recent data not in training, and allows updates without retraining the model.
Governance Advantage
RAG improves explainability because the system can cite which documents informed each response, supporting accountability and auditability requirements.