grounding

Grounding is the practice of anchoring AI output to real, verifiable sources. An ungrounded response is the AI speaking from its training data — which may be outdated, biased, or wrong. A grounded response is the AI synthesizing specific documents you've provided, with the ability to cite its sources.

Grounding is the practical antidote to hallucination. When you tell an AI 'only answer based on the provided documents,' you're grounding it. When you ask it to 'cite the specific section,' you're enforcing grounding. RAG is the technical architecture; grounding is the principle.

For AI operators, grounding is a trust mechanism. It's how you build AI systems that stakeholders actually believe — because every claim can be traced back to a source.

When to Use It

Whenever AI output will be used for decisions — customer-facing content, legal documents, financial analysis, medical information.

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Why It Matters

Ungrounded AI is an opinion machine. Grounded AI is a research assistant. The difference determines whether people trust your AI systems.

Memory Trick

Like grounding a wire — connecting it to something solid and real so there's no dangerous floating voltage.

Example Prompts

Common Misuses

• ×Thinking grounding means 'using RAG' — grounding is a broader principle that includes RAG but also manual citation, fact-checking prompts, etc.
• ×Assuming grounded output is always correct — the source documents themselves might be wrong
• ×Using 'grounding' when you mean 'fine-tuning' — they solve different problems

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