Grounding

Grounding in AI refers to techniques that anchor a model's responses to factual, verifiable information sources rather than relying solely on patterns learned during training. A grounded AI system references specific documents, databases, or real-time data when generating responses.

Why grounding is necessary

Language models generate text based on statistical patterns in their training data. They do not "know" facts — they produce text that is statistically likely given the input. This means they can confidently state things that are outdated, incorrect, or entirely fabricated. Grounding addresses this by giving the model access to authoritative information at the time of response.

How grounding works

The most common grounding approach is retrieval-augmented generation (RAG). When a user asks a question, the system first retrieves relevant documents from a knowledge base, then includes those documents in the model's context along with the question. The model generates its response based on the retrieved information rather than relying purely on its training.

Other grounding methods include providing real-time search results, connecting to databases or APIs, and giving the model access to tools that can verify facts.

Types of grounding

• Document grounding: Anchoring responses to specific internal documents, policies, or knowledge bases.
• Web grounding: Using real-time search results to provide current information.
• Data grounding: Connecting to databases or APIs to provide accurate, up-to-date figures.
• Citation grounding: Requiring the model to cite specific sources for each claim.

Grounding vs fine-tuning

Fine-tuning teaches the model new patterns by updating its weights. Grounding provides information at inference time without changing the model. Fine-tuning is better for teaching style and format. Grounding is better for ensuring factual accuracy with changing information.

Evaluating grounded responses

Effective grounding systems measure faithfulness — does the response accurately reflect the retrieved sources? A grounded response should not introduce claims that are not supported by the provided documents. Techniques like citation checking and automated fact verification help maintain quality.