Your company uses a generative AI solution.

Grounding is an AI solution pattern used to improve response quality by ensuring the model’s output is based on trusted, relevant information provided at inference time, rather than relying only on what the model “remembers” from training. Therefore, C is correct: grounding anchors responses in specific data sources .

In practical deployments, grounding commonly uses retrieval (often called Retrieval Augmented Generation, or RAG) where the system first finds relevant content from approved sources—such as internal policy documents, product documentation, knowledge bases, or databases—and then includes that content in the prompt context sent to the model. Microsoft’s guidance describes grounding data as information supplied at inference time to help responses become more accurate and relevant because the model is guided by authoritative, up-to-date content that may not have been part of original training.

The other options do not define grounding. A relates to inclusion practices and diversity considerations, which are important for responsible AI but are not what grounding means. B describes transparency/explainability concepts. D relates to model evaluation/communication of limitations. Grounding is specifically about tying outputs to known sources, which reduces hallucinations and improves business trust in the generated responses.