Also known as: grounded generation · citation grounding
Grounding is the practice of tying a model's output to specific verifiable source material, so that claims can be traced to documents rather than resting on the model's parametric memory.
Grounding is the primary practical countermeasure to hallucination. Requiring a model to answer only from supplied passages, and to cite which passage supports each claim, converts an unverifiable assertion into a checkable one.
It is not a guarantee. Models can misattribute claims to sources that do not support them, or blend grounded and ungrounded content within a single response, so citation presence should be treated as an aid to verification rather than proof of accuracy.
Hallucination
A hallucination is output from an AI model that is fluent and confident but factually incorrect or entirely fabricated, such as an invented citation, a non-existent API method, or a plausible-sounding but false statistic.
Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) is a technique in which relevant documents are retrieved from an external knowledge source and supplied to a language model as context, so that its response is grounded in specific retrieved material rather than parametric memory alone.
AI Evaluation
AI evaluation is the practice of systematically measuring model performance against defined tasks and criteria, using benchmarks, human review, or automated graders to quantify capability and detect regressions.
Semantic Search
Semantic search retrieves results based on meaning rather than keyword matching, using embeddings to find documents conceptually related to a query even when they share no literal terms.