AI & Agentforce
RAG and grounding, explained simply
Give an AI response relevant business knowledge instead of relying only on model memory.
By Vishal Verma · Reviewed October 5, 2026
Grounding provides relevant information for an AI response. Retrieval augmented generation, or RAG, is one grounding pattern: retrieve useful source material, add it to the prompt context, then generate a response.
Example: a service manual question
- A technician asks how to troubleshoot an equipment fault.
- A retriever finds relevant passages from approved manuals or knowledge articles.
- The model uses those passages to draft a response.
- The experience presents source references so the technician can verify the guidance.
Retrieval does not retrain the model. Good results depend on source quality, access controls, document freshness, and whether the search finds the right evidence. If evidence is missing, the agent should state uncertainty or escalate.
When to use it
RAG is useful for questions over manuals, policies, and knowledge articles. A live stock check or record update generally needs a structured query or action instead. Grounding improves relevance; it does not guarantee correctness.