While general-purpose AI models like ChatGPT have dominated the conversation the past couple years, we’re witnessing a significant shift from general-purpose models to more specialized, custom-built GPTs. These personalized AI models are revolutionizing how we interact with technology, share knowledge, and leverage expertise.
At the forefront of this trend is Guy Kawasaki, Chief Evangelist of Canva and former Chief Evangelist of Apple, with his innovative KawasakiGPT. In this post, we'll explore the process of creating a custom GPT, using KawasakiGPT as a case study, and delve into the potential applications, challenges, and future of personalized AI in both personal and professional contexts.
Creating a custom GPT like KawasakiGPT involves several crucial steps, each contributing to the model's individuality and effectiveness.
Like any AI model, the foundation of any custom GPT is its data. For KawasakiGPT, Kawasaki took a comprehensive approach, incorporating a wide range of content. This includes personal writings, videos, blog posts, and importantly, transcripts from his podcast featuring conversations with over 250 remarkable individuals across various fields.
This diverse dataset ensures that KawasakiGPT isn't just a reflection of Kawasaki's personal knowledge, but a synthesis of insights from a wide range of experts. When building your own GPT, you might consider including:
The key is to curate high-quality, relevant data that accurately represents your expertise and the knowledge you want your GPT to embody.
When creating a custom GPT like KawasakiGPT, you don't typically choose the base model directly. Instead, you select a service provider that offers customization options for their proprietary language models. For instance:
These providers don't give direct access to their base models. Rather, they offer interfaces and tools that allow you to build on top of their existing models. The choice of provider depends on factors such as:
Most custom GPTs are built on one of these major platforms, leveraging powerful base models while adding his unique dataset and fine-tuning process.
Once you have your data and base model, the next step is fine-tuning. This process involves training the model on your curated dataset, allowing it to learn the nuances of your expertise and communication style. For KawasakiGPT, this likely involved multiple iterations to capture Kawasaki's unique voice and the diverse insights from his podcast guests.
Rigorous testing is crucial to ensure your custom GPT performs as expected. This involves:
The goal is to reach a point where the model's responses are on par with or even superior to what you might provide personally, as Kawasaki found with his GPT.
Finally, your custom GPT needs to be deployed in a user-friendly interface. For KawasakiGPT, this involves a platform where users can easily interact with the AI, asking questions and receiving responses in Kawasaki's style.
For those interested in creating custom GPTs themselves, this process provides a comprehensive framework. However, if you're looking for a more straightforward approach, custom GPTs can also be created within ChatGPT, offering ease of use with less control over fine-tuning. Alternatively, for a hands-off solution, enterprise-level options like Betty Bot cater specifically to associations, providing comprehensive, tailored AI solutions.
KawasakiGPT stands out for several reasons:
The applications of custom GPTs like KawasakiGPT are vast and varied:
While the potential of custom GPTs is exciting, there are several challenges to consider:
Looking ahead, the future of custom GPTs is bright and full of possibilities:
The real concern for the future isn't that AI will replace humans entirely, but rather that those who can effectively leverage AI will have a significant advantage over those who can't. This underscores the importance of not just creating custom GPTs but learning to use them strategically and efficiently.
Custom GPTs like KawasakiGPT represent a real leap forward in how we can AI can be used for personal and professional growth. In crafting AI models that encapsulate specialized knowledge and unique perspectives, new possibilities for knowledge sharing, innovation, and personalized interactions present themselves.
Guy Kawasaki's approach demonstrates that the key to success lies in curating high-quality data, continuously refining the model, and finding creative applications that add real value. Despite the challenges custom GPTs present, the potential benefits are too significant to overlook.
Whether you're an individual looking to scale your influence, an association aiming to better serve your members, or a business seeking to innovate, exploring the possibilities of custom GPTs could be a game-changer. As Kawasaki aptly put it, "I don't know how you could maximize and optimize the educational services that you provide as an association to your customer without using AI at this point." The future of personalized AI is here, and it’s time to embrace it’s potential.
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