Reading the Object, Not the Story Told About It

Opening Scene

The single most reliable thing an appraiser does is examine the object itself directly — its actual materials, its actual construction — rather than relying on the story someone tells about it. This direct examination is what separates genuine appraisal from simply repeating a plausible narrative. Grounding a language model’s claims in actual retrieved source material plays this same decisive role in reducing hallucination.

In Plain English

Grounding means directing a model to base its answer on actual retrieved source documents, rather than generating a response purely from its trained parameters, connecting directly to the retrieval-augmented generation techniques covered in this content library’s dedicated RAG series. A grounded model can cite the actual source it drew a claim from, and critically, can recognize when the retrieved sources genuinely don’t contain an answer, rather than fabricating one to fill the gap.

The Old Way

Before grounding was widely recognized as the primary, most effective hallucination mitigation, models often answered purely from trained knowledge:

• Models often answered questions purely from their trained parameters, without any mechanism for checking those answers against actual, current source material.
• There wasn’t yet a well-established practice of designing systems to explicitly recognize and communicate when no genuine answer was available in the retrieved sources.
• Users had no reliable way to trace a model’s specific claim back to an actual, verifiable source document.

Grounding emerged specifically as retrieval-augmented generation matured into a practical, reliable technique, directly addressing hallucination at its root by anchoring claims to real, checkable material.

What’s Changing (and Why AI Is the Reason)

1. Grounded generation, extending the retrieval-augmented generation techniques covered in this content library’s dedicated RAG series, has become the primary, most effective hallucination mitigation strategy.
2. This connects directly to the source attribution and citation practices covered in Article 6, which are what make a grounded claim genuinely verifiable.
3. Well-designed grounded systems increasingly recognize and communicate when retrieved sources don’t contain a genuine answer, rather than fabricating one.

The Metaphor, Fully Extended

The Antiques Appraiser	Grounding Concept
Examining the object’s actual materials directly	Basing a claim on actual retrieved source documents directly
Not relying on the story someone tells about it	Not relying purely on a model’s trained parameters alone
Direct examination separating appraisal from narrative repetition	Grounding separating verified claims from fabricated ones
Recognizing when there’s genuinely nothing to authenticate	Recognizing when retrieved sources genuinely don’t contain an answer

Quick Recap

• Grounding directs a model to base its answer on actual retrieved source documents, not purely trained parameters.
• This extends the retrieval-augmented generation techniques covered in this content library’s dedicated RAG series.
• A well-grounded model can cite its actual source and recognize when no genuine answer is available.
• Grounding is the primary, most effective strategy for reducing hallucination.

Where This Fits in the Series

Article 4 covered grounding as the primary mitigation strategy. Article 5 turns to the different kinds of hallucination, distinguishing an outright forgery from a genuine, honest guess.