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Grounding

Constraining an LLM to base its answers on verified source data (documents, databases, search results) rather than on its parametric memory alone.

What Is Grounding?

An ungrounded model answers from training data and confidently invents specifics. Grounding feeds the model authoritative context at query time and instructs it to answer only from that context, with citations. RAG is the most common grounding technique, but grounding also covers tool calls to live systems and database lookups. Grounding is the single biggest lever against hallucination in production AI products.

How Groovy Web Uses This

We ground client AI products in their own data with retrieval and citations, so answers trace back to a source the user can verify rather than to the model guessing.

Need Help with This?

Our AI-First engineers build production systems using Grounding technology. Talk to us.

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