Grounding vs RAG
Overview
Grounding and retrieval-augmented generation (RAG) are related but hierarchically distinct concepts that are often used as synonyms. Grounding is the goal: constraining a model's output to be supported by authoritative external information rather than relying solely on parametric knowledge. RAG is a specific architecture that achieves grounding by retrieving relevant documents and supplying them to the model as context at generation time.[1][2]
The relationship is one of containment: grounding is the broader objective; RAG is one means; document-grounding and tool-based grounding are alternatives or variants.
How it works
- Grounding (goal): achieved by any method that ties output to sources — retrieval, supplied documents, tool calls, or constrained decoding.
- RAG (method): a retriever selects documents from a corpus or index; the model generates an answer conditioned on those documents, ideally citing them.
- Document-grounding (variant): the source documents are supplied directly (for example a user-provided file) rather than retrieved from an index.
Whether grounding succeeds is assessed by groundedness — whether each claim is actually supported by the provided context.
| Term | Role |
|---|---|
| Grounding | The objective: output supported by sources |
| RAG | An architecture that retrieves then generates |
| Document-grounding | Grounding on directly supplied documents |
| Groundedness | The evaluation of whether grounding succeeded |
RAG is not the only way to ground a model, and grounding is not guaranteed by using RAG: a RAG system can still produce ungrounded claims if the model ignores or misreads the retrieved context.
Examples
- A support bot that retrieves help-center articles and answers from them is a RAG implementation of grounding.
- Pasting a contract into a chat and asking questions about it is document-grounding without retrieval.
See also
References
- ↑ IBM. "What is Retrieval-Augmented Generation (RAG)?" https://www.ibm.com/think/topics/retrieval-augmented-generation
- ↑ Lewis, P. et al. (2020). "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks." arXiv:2005.11401. https://arxiv.org/abs/2005.11401
