Grounding

Simple Definition

Grounding means anchoring an AI’s responses to real, verifiable information — actual documents, databases, or live data — rather than relying purely on what the model has memorized from training.

A grounded AI response is traceable back to a real source. An ungrounded response comes entirely from the model’s internal knowledge, with no external verification.

Why Grounding Matters

LLMs can hallucinate — stating false information with confidence. Grounding is one of the most effective ways to reduce this:

Ungrounded: “The company was founded in 1987.” (The AI might be wrong.)

Grounded: “According to the company’s About page: ‘Founded in 1987…’” (The AI cites a real source.)

How Grounding Works

1. Retrieve relevant documents from a knowledge base, database, or web search
2. Include those documents in the AI’s context (as part of the prompt)
3. Instruct the AI to base its answer only on the provided documents
4. Request citations so the user can verify

This is the core principle behind RAG (Retrieval-Augmented Generation).

Levels of Grounding

• Document grounding — AI answers based on specific uploaded documents
• Knowledge base grounding — AI retrieves from a curated internal database
• Web grounding — AI searches the live web for current information (Perplexity, Bing Chat)

Grounding vs. Fine-Tuning

Fine-tuning bakes knowledge into the model permanently. Grounding provides information at query time. Grounding is more flexible — the source documents can be updated without retraining.

Related Terms

• Hallucination — what grounding helps prevent
• RAG — retrieval-augmented generation, the main technical implementation of grounding
• AI Safety — grounding is a key reliability and safety technique
• LLM — the models that benefit from grounding