Grounding connects LLM outputs to verified external sources like documents, databases, or APIs. This reduces hallucinations by ensuring the model's responses are based on factual, retrievable information rather than just parametric knowledge.
Grounding Techniques
- RAG: Retrieve relevant documents before generation
- Tool use: Call APIs for real-time data
- Citations: Require sources for claims
- Knowledge graphs: Structured fact verification
Grounding Limitations
Grounding helps but doesn't solve everything:
- Models can misinterpret retrieved sources
- Can hallucinate connections between facts
- May generate claims not supported by sources
- Still requires monitoring for accuracy
Does grounding eliminate hallucinations?
No. Grounding significantly reduces hallucinations but doesn't eliminate them. Models can still misinterpret sources, hallucinate connections, or generate unsupported claims. Monitor grounded outputs for factual accuracy.
Practical example
An answer names the policy passage that supports the claimed return period.
What to check
Open the cited source, confirm that it supports the exact claim, and record the document version.
Limits and tradeoffs
A retrieved passage can be outdated, irrelevant, or wrong even when the response cites it.
Maintained by DriftRail. AI assisted the October 2026 content and template revision. Examples are illustrative; they are not measured product benchmarks. Suggest a correction.