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The article addresses critical architectural strategies for deploying reliable AI in enterprise support by combining Retrieval-Augmented Generation (RAG) with fine-tuning to mitigate hallucinations and context limitations. This hybrid approach directly tackles the practical constraints of current Large Language Models, focusing on data security, latency optimization, and maintaining domain-specific accuracy rather than general consumer applications.

Surse care acoperăKDnuggets, SiliconANGLE

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Beyond Bots: Rethinking AI Support with a Hybrid AI Architecture

KDnuggets6 august 20266 min de cititArticolul și perspectiva sunt în engleză.

Learn how blending RAG and fine-tuning creates more effective AI support experiences.

Recently a customer needed a chatbot developed that was safe, accurate, and capable of near real-time responses. In simple terms, they needed an AI system that could answer support queries flawlessly, without exposing sensitive data or drifting away from the company's voice. Requests like this are now more frequent, as data security, latency, and response quality directly impact a company's bottom line. AI systems that get these requirements wrong lose trust quickly and cost the business money. IBM's 2025 report estimates the average global cost of a data breach at \$4.44 million. But generic chatbots and off-the-shelf large language models (LLMs) often fail to meet enterprise expectations. There's no doubt that LLMs are powerful. But these models face real…

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