Connecting an AI model's answers to verified sources or real data so its responses are based on facts rather than guesswork.
Language models generate text from patterns learned during training, which can lead to outdated or invented information. Grounding reduces this by supplying the model with trusted, relevant material at the time of the request, such as company documents, database records, or live search results, and instructing it to base its answer on that material.
A common example is an HR assistant that answers leave policy questions only from the current employee handbook, and cites the section it used. Retrieval-augmented generation is the most widely used grounding technique. Grounding improves accuracy and makes answers easier to check, but it depends on the quality of the sources. If the documents are wrong or out of date, or the retrieval step finds the wrong passage, the model can still give a poor answer.
When an AI model produces information that sounds plausible but is false, fabricated, or not supported by its source material.
Retrieval-Augmented GenerationA technique that improves AI answers by first retrieving relevant documents and then giving them to a language model as context.
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