Grounding
Definition
Grounding anchors AI outputs in concrete external information or sources.
In grounding, a model receives relevant data as context or is checked against a known knowledge source. RAG is a common grounding technique, but grounding can also include other data or tool accesses.
Simply explained
Like looking up in approved documents before answering and aligning the answer accordingly.
Why it matters in practice
Highly relevant for corporate knowledge because answers can become more traceable and up-to-date. However, grounding does not completely prevent errors.
Example from business
An internal assistant answers a question only based on approved manuals and cites the used references.
Limits and pitfalls
- Equating grounding with factual accuracy; using poor or outdated sources; forgetting rights and source verification; filling missing hits with fabricated answers.
Origin & history
Grounding is an established term in current LLM and RAG systems. Google defines grounding as basing an LLM answer on information from trusted sources.
Use in mid-sized companies
- Possible applications: knowledge assistants, support research, policy queries, and document-supported answers.
Frequently asked questions
Is a source-bound answer automatically true?
No, the source can also be wrong or misunderstood. Is RAG the same as grounding? RAG is a common method for it, but grounding is broader. What does groundedness mean? The property or degree to which an output is based on sources; grounding denotes the process.
Sources
- Grounding und Groundedness · Google Machine Learning Glossary, Grounding und Groundedness
Source check: 11.10.2026.
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