Grounding
Grounding is the practice of connecting an LLM's output to external, verifiable information sources—such as retrieved documents, live web results, or structured databases—to reduce hallucination and improve factual accuracy.
Grounding, in the context of large language models, is the practice of anchoring model outputs to specific external information sources—such as retrieved documents, live web search results, structured databases, or API responses—so that claims can be traced to verifiable evidence. It directly addresses hallucination, the tendency of parametric LLMs to generate plausible-sounding but factually incorrect statements based on statistical patterns in training data rather than authoritative or real-time facts.
The most prevalent grounding mechanism is Retrieval-Augmented Generation (RAG), introduced by Lewis et al. at NeurIPS 2020, where relevant passages are fetched from a knowledge base and inserted into the model prompt before generation. Alternative approaches include tool use and function calling—where the model invokes a search API, calculator, or SQL query and incorporates the result—and structured data grounding, where responses are derived directly from database query outputs. Each approach varies in latency, coverage, and auditability of the evidence chain.
Grounding is essential for enterprise deployments where accuracy and accountability are required. It enables citations: the model can reference the exact source passage it used, supporting human verification and regulatory compliance. It also allows models to handle events occurring after their training cutoff, since relevant documents can be retrieved at inference time without retraining.
As of 2026, grounding is a default feature in major AI assistants. ChatGPT integrates Bing web search, Google Gemini uses Google Search grounding natively, and Perplexity AI is built entirely around a search-grounded architecture. Enterprise platforms such as Microsoft 365 Copilot and Salesforce Einstein ground responses against proprietary organizational data via RAG pipelines. Dedicated evaluation frameworks—RAGAS and ARES—specifically measure grounding quality through metrics such as faithfulness and answer relevance.