Grounding
Connecting AI responses to authoritative sources or real-world data to reduce hallucinations and improve accuracy.
From the archive. This restored glossary entry is educational, not a current vendor comparison. Product names, models, capabilities, pricing, and availability may have changed; verify operational details with primary sources.
Why Grounding Matters
Ungrounded AI makes things up. It generates plausible text without checking facts. Grounding forces the AI to reference actual information.
Ungrounded: "The company was founded in 2018." (might be wrong)
Grounded: "According to their website, the company was founded in 2019." (verifiable)
Grounding Methods
Document retrieval (RAG): Give the AI relevant documents to reference.
Web search: Let the AI search and cite web sources.
Database queries: Connect to authoritative data sources.
API access: Call services that provide verified information.
Benefits of Grounding
- Fewer hallucinations
- Verifiable claims with sources
- More current information
- Domain-specific accuracy
Limitations
Grounding helps but doesn't eliminate errors:
- Retrieved sources might be wrong
- AI might misinterpret sources
- Sources might be outdated
- AI might ignore grounding for some responses
Evaluating Grounded AI
When reviewing AI tools that claim grounding:
- Does it actually cite sources?
- Can you verify the citations?
- How current are the sources?
- What happens when sources conflict?
Good grounding is transparent. You should see where information comes from.