*This article contains information sourced from Nvidia
Generative AI refers to a class of artificial intelligence models designed to create new content, such as text, images, music, and even code, by learning patterns from existing data. Unlike traditional AI, which typically performs classification or prediction tasks, generative AI can produce original and realistic outputs that mimic human creativity.
“The demand for generative AI technologies is driving significant revenue potential and increased IT spending on enhanced infrastructure and software. Enterprises that adopt next-generation AI, such as large language models (LLMs) and generative AI, are 2.6 times more likely to boost their revenue by 10% or more. However, to fully capitalize on these benefits, they must invest in robust AI infrastructure.” – Accenture Research. Breakthrough Innovation: Is your organization equipped for breakthrough innovation?” WEF 2023
Benefits of Adopting Generative AI
Generative AI can be adopted across industries by integrating it into creative processes, such as content creation in media and entertainment, personalized marketing in retail, and drug discovery in pharmaceuticals. Additionally, businesses can leverage generative AI for designing innovative products, automating customer service, and enhancing data analysis and decision-making across various sectors.
Generative AI Adoption Across Industries
Connecting Generative AI to Enterprise Data
Connecting Generative AI to Enterprise Data using foundation models and Retrieval-Augmented Generative Response (RAG) frameworks involve in several sophisticated techniques designed to maximise the utility of AI in handling vast and complex enterprise datasets.
- Understand Enterprise Data Needs: Start by identifying the specific business problems and data requirements of your enterprise, ensuring that the AI solution addresses these needs effectively.
- Select a Suitable Foundation Model: Choose a robust foundation model that can be fine-tuned or customized based on your enterprise data to ensure high accuracy and relevance in responses.
- Integrate Data Sources: Connect the foundation model to various enterprise data sources, such as databases, APIs, and data lakes, to enable comprehensive data retrieval and utilization.
- Implement Retrieval-Augmented Generation (RAG): Use a RAG approach, where the model first retrieves relevant information from the enterprise data source and then generates responses based on this information, enhancing accuracy and relevance.
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LLMs are Powerful Tools but Not Accurate Enough for Enterprise
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