Grounding

Grounding connects an LLM's outputs to verifiable, real-world information sources. A grounded model response is tied to specific, citable facts rather than relying purely on parametric knowledge, reducing hallucination and increasing trustworthiness.

What is Grounding?

Grounding refers to the practice of anchoring an LLM's responses in external, verifiable information rather than letting the model rely exclusively on what it has memorised during training. A fully grounded response cites its sources, and every factual claim can be traced back to a retrieved document.

Methods of Grounding

  • RAG — Retrieve documents at query time and inject them into the prompt. The model is instructed to answer only from the provided context.
  • Web search integration — Connect the model to live web search (e.g. Bing, Google, Brave) so it can retrieve current information.
  • Database grounding — The model queries a structured database for facts before responding.
  • Citation generation — Require the model to produce inline citations for every factual claim, making verification easy.

Grounding vs Context

Grounding is about connecting responses to external truth, while the context window is simply the space available. You can have a large context without grounding (e.g. a long conversation) and you always need grounding if factual accuracy is critical.

Grounding in Production Systems

Google's Vertex AI offers a "grounding with Google Search" feature. Bing Copilot uses real-time web grounding. Perplexity AI is built entirely on a grounding-first architecture that requires every claim to be cited.

Example

An enterprise legal assistant is grounded in the company's internal contract database. Every clause it cites is linked to an actual contract, making its answers auditable and legally defensible — unlike a hallucinating general-purpose chatbot.

Related terms

  • RAG (Retrieval-Augmented Generation) RAG is a technique that combines an LLM with an external knowledge retrieval system. Instead of relying solely on its training data, the model retrieves relevant documents at query time and uses them as context to generate more accurate, up-to-date responses.
  • Hallucination Hallucination is when an AI language model generates plausible-sounding but factually incorrect or entirely fabricated information. The model 'hallucinates' facts, citations, names, or events that do not exist, presenting them with false confidence.
  • Function Calling (Tool Use) Function calling allows LLMs to output structured calls to external functions or APIs instead of free-text responses. The model selects the appropriate function, fills in parameters from the user's request, and returns a structured JSON payload that your code can execute.
  • LLM (Large Language Model) A Large Language Model is a neural network trained on massive text corpora to predict and generate text. LLMs like GPT-4, Claude, and Gemini exhibit broad language understanding, reasoning, and generation capabilities that emerge from scale.