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Most enterprise RAG initiatives succeed as proofs of concept but struggle in production. Learn why cost overruns, governance failures, and scalability issues emerge, and how a production-grade RAG pipeline helps organizations achieve sustainable AI outcomes.
Retrieval-Augmented Generation, or RAG, has moved from experimental architecture to mainstream enterprise infrastructure. Across U.S. organizations, it now supports customer service bots, compliance assistants, internal knowledge systems, financial planning workflows, support automation, and executive decision tools.
The promise is easy to understand. Instead of asking a large language model to answer from general training data alone, RAG grounds outputs in enterprise content. It retrieves relevant documents, knowledge-base articles, operational data, or policy records before generating a response. In theory, that should reduce hallucinations and make generative AI more useful in real business environments.
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