Custom AI Applications Explained w/Daniel Becker
Summary
In this conversation, Nils and Daniel explore the impact of AI on user experiences, focusing on custom AI applications and their integration with data management systems. They discuss the mechanics of AI models, the importance of structured content, and the implications of data privacy when using public APIs. The dialogue provides insights into the evolving landscape of AI technology and its practical applications in business. In this conversation, Nils and Daniel explore the complexities of using AI in business, focusing on data sensitivity, model selection, and the importance of understanding context windows. They discuss the role of Retrieval-Augmented Generation (RAG) in enhancing AI applications and share insights on the future of user experience with AI technologies. The dialogue emphasizes the need for careful consideration when integrating AI into business processes, particularly regarding data privacy and the evolving nature of user interfaces.
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About the author
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Hi, I'm Nils, Co-Founder and Head of Interaction Design at Dinghy. With over 10 years of experience in designing and building websites, I'm passionate about creating great digital experiences. At Dinghy, we believe that exceptional UX comes from the synergy of branding, content strategy, design, and development, all working together seamlessly.
On this channel, I show you how to create outstanding user experiences for your customers through hands-on, easy-to-follow how-tos in wireframing, UI design, stakeholder management, usability testing, HTML, CSS, and frontend development.
Keywords
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user experience, digital products, customer satisfaction, business profitability, user testing, research, product market fit, development costs, customer lifetime value, app store ratings, marketing spend, conversion rates, employee efficiency, product features, ux, design, usability, test, persona, css, product development, AI, user experience, custom applications, data management, AI models, data privacy, LLMs, structured content, prompt engineering, machine learning, AI, data sensitivity, model selection, context windows, RAG, user experience, technology, innovation, machine learning, applications
In this conversation, Nils and Daniel explore the impact of AI on user experiences, focusing on custom AI applications and their integration with data management systems. They discuss the mechanics of AI models, the importance of structured content, and the implications of data privacy when using public APIs. The dialogue provides insights into the evolving landscape of AI technology and its practical applications in business. In this conversation, Nils and Daniel explore the complexities of using AI in business, focusing on data sensitivity, model selection, and the importance of understanding context windows. They discuss the role of Retrieval-Augmented Generation (RAG) in enhancing AI applications and share insights on the future of user experience with AI technologies. The dialogue emphasizes the need for careful consideration when integrating AI into business processes, particularly regarding data privacy and the evolving nature of user interfaces.
📲 Keep in touch with Dinghy:
🔗 Subscribe to our YouTube: https://www.youtube.com/@dinghystudio
🔗 Follow us on Instagram: https://www.instagram.com/dinghystudio/
🔗 Follow us on LinkedIn: https://www.linkedin.com/company/dinghystudio/
🔗 Read our blog: https://www.dinghy.studio/blog/
About the author
---
Hi, I'm Nils, Co-Founder and Head of Interaction Design at Dinghy. With over 10 years of experience in designing and building websites, I'm passionate about creating great digital experiences. At Dinghy, we believe that exceptional UX comes from the synergy of branding, content strategy, design, and development, all working together seamlessly.
On this channel, I show you how to create outstanding user experiences for your customers through hands-on, easy-to-follow how-tos in wireframing, UI design, stakeholder management, usability testing, HTML, CSS, and frontend development.
Keywords
---
user experience, digital products, customer satisfaction, business profitability, user testing, research, product market fit, development costs, customer lifetime value, app store ratings, marketing spend, conversion rates, employee efficiency, product features, ux, design, usability, test, persona, css, product development, AI, user experience, custom applications, data management, AI models, data privacy, LLMs, structured content, prompt engineering, machine learning, AI, data sensitivity, model selection, context windows, RAG, user experience, technology, innovation, machine learning, applications
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