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Generative AI

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Fine-tuning large language models adapts pre-trained models to specific tasks or domains using tailored datasets, while Retrieval-Augmented Generation (RAG) combines retrieval systems with generative models to dynamically incorporate external, up-to-date knowledge into outputs.

RAG vs Fine-Tuning: Differences, Benefits, and Use Cases Explained

Edge AI Technology Report: Generative AI Edition Chapter 1. Generative AI's demand for real-time insights is driving a shift from cloud to edge computing, enabling faster, local processing on devices and reducing cloud latency and bandwidth constraints.

Leveraging Edge Computing for Generative AI

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