What is Knowledge-Grounded Generation?
A generative AI approach where outputs are grounded in external knowledge sources, such as documents or databases.
More about Knowledge-Grounded Generation:
Knowledge-Grounded Generation combines the strengths of retrieval-based models and generative AI to produce outputs that are contextually accurate and based on external knowledge. This approach typically involves fetching information from sources like vector databases or knowledge retrieval and using it to generate responses.
It is especially useful for applications like retrieval-augmented generation, customer support bots, and question answering systems, ensuring that responses are both relevant and factually correct.
Frequently Asked Questions
How does knowledge-grounded generation improve response accuracy?
It ensures that generated content is based on verified information retrieved from reliable sources like knowledge graphs.
What AI models are commonly used for knowledge-grounded generation?
Models like GPT and BERT are often paired with retrieval augmentation pipelines for this purpose.
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