Grounding
LLM & Language ModelsTechniques that anchor AI responses in verifiable facts and sources — reducing hallucination by connecting model outputs to real data.
Grounding is the practice of connecting AI outputs to verifiable information sources. An ungrounded AI generates from its training data (which may be wrong or outdated). A grounded AI references specific documents, databases, or real-time information.
RAG is the most common grounding technique — retrieving relevant documents before generating. Web search integration (Perplexity, ChatGPT Browse) grounds responses in current information. Citation requirements force the model to link claims to sources.
Grounding is essential for enterprise AI. A legal chatbot must ground responses in actual law. A medical AI must reference real clinical guidelines. A customer support bot must cite actual product documentation. Without grounding, AI is just confidently guessing.
Real-World Example
Perplexity grounds every response in cited web sources — you can verify any claim by clicking the citation. That's grounding in action, and it's why Perplexity is trusted for research.
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FAQ
What is Grounding?
Techniques that anchor AI responses in verifiable facts and sources — reducing hallucination by connecting model outputs to real data.
How is Grounding used in practice?
Perplexity grounds every response in cited web sources — you can verify any claim by clicking the citation. That's grounding in action, and it's why Perplexity is trusted for research.
What concepts are related to Grounding?
Key related concepts include RAG (Retrieval-Augmented Generation), Hallucination. Understanding these together gives a more complete picture of how Grounding fits into the AI landscape.