Grounding refers to the ability of generative AI models to ensure that the content they produce is anchored in factual information, typically drawn from verified sources such as documents, knowledge bases, or databases. In generative applications, grounding helps to connect the AI’s output to real-world, reliable information, ensuring that the generated responses or completions are not only coherent but also accurate and truthful.
This process involves linking the AI’s generated content to these factual sources, either by citing the original reference directly or by searching for external data to validate or update the response. Grounding plays a crucial role in enhancing the credibility of generative models, especially when the AI is tasked with producing information in fields like healthcare, legal, or finance, where accuracy is paramount. It helps prevent errors, inaccuracies, or hallucinations by ensuring that the AI’s outputs are firmly rooted in reality.