Retrieval-augmented generation breaks at scale because organizations treat it like an LLM feature rather than a platform discipline. Enterprises that succeed with RAG rely on a layered architecture.
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Hallucinations in LLMs: Technical challenges, systemic risks and AI governance implications Editor's note: The IAPP is policy neutral. We publish contributed opinion and analysis pieces to enable our ...
Abstract: Retrieval augmented generation (RAG) improves the accuracy and dependability of generative AI models by integrating factual information from external databases. This technique is widely used ...
A controversy is swirling at a Texas university. The trigger? A flowchart. On Dec. 1, the new chancellor of the Texas Tech University system sent professors a diagram laying out a chain of approval ...
WASHINGTON, Dec. 8, 2025 /PRNewswire/ -- OODA, a leader in strategic advisory and research at the intersection of technology, national security, and business, is pleased to announce the launch of OODA ...
Despite Large Language Models (LLMs) have demonstrated astonishing capabilities across various tasks, they still face limitations when dealing with specialized and knowledge-intensive tasks, such as ...
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