Customizing Generative AI: Top Techniques for Unique Value
Survey Reveals Ambitious Approach to Customization
We surveyed 300 technology leaders in mostly large organizations in different industries to learn how they are seeking to leverage generative AI models and applications. The results show that companies are moving ahead with customization, aware of its risks, particularly around data security, but employing advanced methods and tools to realize their desired customization gains.
Motivations and Methods
The surveyed leaders are customizing generative AI models and applications to achieve specific business objectives, such as improving customer experience, streamlining processes, and enhancing decision-making. They are employing a range of methods, including retrieval-augmented generation (RAG), to achieve their goals.
Challenges and Solutions
While customizing generative AI models and applications, companies are encountering difficulties, particularly around data security and data quality. To overcome these challenges, they are employing advanced methods and tools, such as RAG, to ensure effective and secure customization.
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Conclusion
Our analysis finds that companies are moving ahead with customization, aware of its risks, and employing advanced methods and tools to achieve their desired customization gains. By understanding the motivations, methods, and challenges associated with customizing generative AI, organizations can better navigate the process and realize unique value.
Frequently Asked Questions
- What is generative AI, and how is it being used?
Generative AI refers to the use of artificial intelligence (AI) models to generate new content, such as text, images, or videos, based on patterns and algorithms. - What are the benefits of customizing generative AI?
Customizing generative AI can help organizations improve customer experience, streamline processes, and enhance decision-making. - What are the challenges associated with customizing generative AI?
Companies may encounter challenges around data security, data quality, and the need for advanced methods and tools, such as retrieval-augmented generation (RAG), to achieve effective and secure customization.
