AI Glossarytechniques

Grounding (AI)

Connecting AI model outputs to verifiable sources of truth — such as retrieved documents, databases, or real-time data — to reduce hallucination and increase factual accuracy.

How It Works

Grounding solves the fundamental problem of AI hallucination. An ungrounded model generates answers from its training data, which may be outdated, incomplete, or simply wrong. A grounded model generates answers based on specific, provided evidence. The most common grounding technique is RAG: retrieve relevant documents and include them in the prompt, so the model bases its answer on real data rather than memory. But grounding goes beyond RAG: (1) Web search grounding — the model searches the web for current information before answering. OpenAI's Responses API has this built in. (2) Database grounding — the model queries a structured database for facts. (3) Tool-use grounding — the model calls an API to get real-time data (weather, stock prices, flight status). (4) Citation grounding — the model must cite specific sources for every claim, and unsupported claims are flagged. For production systems, grounding is not optional — it is a requirement. Users lose trust quickly when an AI makes up information. Implement grounding by: providing relevant context with every query, requiring citations in the output, validating claims against source data, and being transparent when the model is uncertain.

Common Use Cases

  • 1Enterprise search and Q&A
  • 2Legal and medical AI (where accuracy is critical)
  • 3Real-time information retrieval
  • 4Customer support with verified answers
  • 5Report generation with citations

Related Terms

RAG (Retrieval-Augmented Generation)

A technique that enhances AI responses by retrieving relevant information from a knowledge base before generating an answer.

Hallucination

When an AI model generates information that sounds plausible but is factually incorrect, fabricated, or not grounded in its training data.

Semantic Search

A search approach that finds results based on meaning rather than exact keyword matches, using embeddings to understand the intent behind queries.

AI Hallucination Detection

Techniques and systems for identifying when an AI model generates false, fabricated, or unsupported information that appears plausible.

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