Grounding

Grounding is the practice of constraining an AI model's output to verified source material — your documents, your data, or live search results — so that responses are anchored in fact rather than drawn from the model's training memory. A grounded model answers from evidence; an ungrounded one answers from recollection.

Why it matters

For marketing work, grounding is the difference between an assistant that describes your product accurately and one that describes a plausible product that doesn't exist. Ungrounded generation is where pricing errors, invented features, and fabricated statistics come from. Any AI system that speaks about your brand — internal copy assistants, support bots, content agents — should be grounded in approved source material, full stop.

How it's used

The most common implementation is RAG: retrieve relevant passages from a knowledge base, inject them into the prompt, and instruct the model to answer only from what was retrieved. Simpler forms count too — pasting the product spec into the prompt grounds a one-off task. Mature teams maintain a curated "source of truth" corpus (approved claims, current pricing, brand messaging) and require every brand-facing AI output to be grounded in it, with citations back to the source passage so humans can verify quickly.

Related terms

RAG · Hallucination — grounding is the primary defense against hallucination, and RAG is its standard architecture.