Tag: LLM
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Shadow AI: Identifying and Managing Unapproved LLM Usage in Teams
As generative AI tools become more accessible and powerful, many enterprise teams are experimenting with large language models (LLMs) like ChatGPT, Bard, and Claude to boost productivity, automate tasks, and generate insights. But with this surge in usage comes a rising risk: shadow AI. Just as “shadow IT” describes unsanctioned technology usage outside formal IT governance,…
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The Role of Semantic Layer in LLM-Ready Enterprise Data Stacks
As large language models (LLMs) become integrated into enterprise workflows, the focus has shifted from just building models to making enterprise data ready for them. One of the most critical enablers of this transformation is the semantic layer, a structured, business-friendly abstraction that sits between raw data and applications or models consuming it. Why Enterprises…
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LLM Fine-Tuning vs. Retrieval-Augmented Generation (RAG): What’s Right for Your Business?
Introduction As enterprises adopt generative AI, particularly large language models (LLMs), a key decision arises: Should you fine-tune the model or implement Retrieval-Augmented Generation (RAG)? This choice isn’t just about architecture. It affects everything from performance and cost to compliance and agility. Understanding both options’ strengths, trade-offs, and business implications is crucial for making the…