A software company deploys a generative AI chatbot to answer employee questions about internal HR policy. Because the underlying language model was trained on general public text and has no knowledge of the company's specific, frequently updated policy documents, engineers configure the chatbot to first retrieve relevant passages from the company's internal policy repository and insert them into the prompt before the model generates its response. Which concept does this design apply?

Retrieving relevant policy passages and inserting them into the prompt at the moment of the question, rather than relying on what the model memorized during training, is the definition of grounding through retrieval-augmented generation: it supplies current, source-specific content so the response reflects the company's actual, up-to-date policy rather than the model's general pretraining. The reinforcement-learning answer is incorrect because reinforcement learning involves a model updating its behavior through reward signals over repeated interactions, not retrieving documents at answer time. The fine-tuning answer is incorrect because fine-tuning permanently adjusts the model's internal weights through additional training, whereas this design leaves the model's weights untouched and instead supplies fresh context per request, which is faster to update and does not require retraining whenever policy changes. The prompt-injection answer is incorrect because prompt injection refers to untrusted or malicious content manipulating a model's instructions to produce unintended behavior, whereas here the retrieved content is trusted, deliberately selected company data intended to improve the accuracy of the answer, not hijack it. Grounding is the concept that connects general-purpose language models to an organization's own live information.