Sponsor Content from NVIDIA.
Generative AI tools like ChatGPT, Gemini, and Claude represent significant advancements in the everyday use of AI.
While LLM-powered applications can quickly and accurately respond to a broad range of inquiries, they can be difficult to train and deploy; rely on training data sets that can quickly become outdated; and lack access to proprietary policies, practices, or unique knowledge. By connecting AI models to handpicked data sources, RAG enables faster effective, and case-specific generative AI deployment, while only exposing a targeted portion of enterprise data to AI models.
With this strategy, organizations in any industry can use their own data to build AI agents to support numerous business functions. This may include customer support agents trained on product catalog and customer interaction data, supply chain optimization copilots trained on inventory and demand forecasting data, or even product quality control agents trained on labeled image data and inspection criteria.
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