What is Grounding?
Connecting AI outputs to verified sources of information to reduce hallucinations.
Definition
Grounding is the practice of connecting AI model outputs to verified, authoritative data sources to ensure factual accuracy. Grounding techniques include RAG (retrieving relevant documents), tool use (calling APIs for real-time data), and citation (linking claims to sources). Grounded AI systems hallucinate less and provide verifiable answers.
💡 Example
Perplexity AI grounds every response by searching the web in real-time and citing specific sources. Google Gemini grounds responses using Google Search. Both approaches reduce hallucinations compared to pure LLM responses.
Related concepts
What is Grounding?
Grounding is the practice of connecting AI model outputs to verified, authoritative data sources to ensure factual accuracy. Grounding techniques include RAG (retrieving relevant documents), tool use (calling APIs for real-time data), and citation (linking claims to sources). Grounded AI systems hallucinate less and provide verifiable answers.
How does Grounding work in practice?
Perplexity AI grounds every response by searching the web in real-time and citing specific sources. Google Gemini grounds responses using Google Search. Both approaches reduce hallucinations compared to pure LLM responses.