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The rise of enterprise-grade generative AI has allowed organizations to leverage foundational models from companies like OpenAI and Google DeepMind. However, many businesses have found these models restrictive due to their reliance on public data. Customization has emerged as a solution, enabling companies to adapt large language models to their specific needs by integrating proprietary data and optimizing their functionalities.
A survey of 300 technology leaders reveals a strong trend towards ambitious customization efforts, despite concerns about data security. Companies are increasingly utilizing advanced techniques, such as retrieval-augmented generation (RAG), to enhance their AI models. This shift not only addresses the limitations of existing models but also opens new avenues for creating unique value in various industries.
• Customization enhances generative AI models for specific business needs.
• Companies are using advanced methods like retrieval-augmented generation for AI customization.
Generative AI refers to algorithms that can generate new content based on learned patterns from existing data.
Customization involves adapting AI models to meet specific business requirements by integrating unique data.
RAG combines retrieval of relevant information with generative capabilities to enhance AI responses.
OpenAI develops advanced AI models that serve as foundational technologies for various applications.
Google DeepMind is known for its cutting-edge AI research and development, contributing to foundational models.
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