What is Grounding?
Last updated May 2026Connecting 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
A technique that lets AI access external knowledge bases to provide more accurate answers.
Why this matters
Grounding connects AI outputs to verified sources, reducing hallucination. Tools with built-in grounding (like Perplexity) are more reliable for factual tasks than ungrounded chatbots. Understanding grounding helps you choose the right tool for research and fact-dependent work.
Real-world example
Ask ChatGPT a factual question and you get an answer with no sources — you have to trust the model. Ask Perplexity the same question and you get an answer with clickable citations. NotebookLM goes further by grounding every answer exclusively in your uploaded documents.
See it in action
When an AI model generates plausible-sounding but factually incorrect information.
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.
How does grounding reduce AI hallucinations?
Grounding connects AI responses to verifiable sources like documents, databases, or web search results. Instead of generating answers purely from training data, the model references specific evidence. This makes responses more accurate and allows users to verify claims against the original sources.
What is the difference between grounding and RAG?
RAG is one specific technique for grounding. Grounding is the broader concept of anchoring AI outputs in factual sources, which can include RAG, web search, tool use, or database lookups. RAG specifically retrieves relevant documents and includes them in the model's context.
Which AI tools use grounding effectively?
Perplexity grounds responses in web search results with inline citations. Google Gemini uses Google Search grounding. NotebookLM grounds responses in user-uploaded documents. Enterprise platforms like Azure OpenAI Service offer grounding through custom data connections.