Addressing AI hallucinations with retrieval-augmented generation

Addressing AI hallucinations with retrieval-augmented generation

Artificial intelligence is poised to be perhaps the most impactful technology of modern times. The recent advances in transformer technology and generative AI have demonstrated a potential to unlock innovation and ingenuity at scale.

However, generative AI is not without its challenges, which can significantly hinder adoption and the value that can be created with such a transformative technology. As generative AI models grow in complexity and capability, they also present unique challenges, including the generation of outputs that are not grounded in the input data.

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