OpenAI has pushed model capabilities to an extremely user-friendly level. The integration of multimodal features has reduced the mental or physical integration work I previously needed to do. The next step is to abstract the required task capabilities based on my own workflow and create customized GPTs, which will greatly expand our boundaries of capability.
The Allure of General-Purpose Scenarios#
When using ChatGPT, I was captivated by its versatility—it could help me accomplish various tasks. The advantage of this generality is that I don’t need to learn new usage techniques for each task; I can complete everything through a single chat window.
With the updates to multimodal models, the ecosystem composed of text, images, and plugins saves us time in integrating different information, allowing us to focus more on creation. DALL-E’s drawing capabilities support vague language, meaning that when generating images, I don’t need to use specific Prompt formats; I can simply describe what I want in natural language to get the desired image.
With the search capabilities of plugins like WebPllot, Wikipedia, and Consensus Search, I can access the information I need without opening search engines like Google.
The Promising Future of GPTs#
Before GPTs came along, solving problems required finding the right tools and investing time in learning how to use them. However, with GPTs, we can simply converse in natural language to customize exclusive solutions based on our needs.
If we compare the GPT model to an engine in game development, then GPTs are like plugins attached to that engine, refining the granularity of problem-solving to a deeper level. More importantly, they no longer require programming skills, making them accessible even to those without a programming background.
The Power of Prompt#
“English will become the most excellent programming language of the future.”
This saying has circulated since the rise of AI, and its accuracy is now undeniable. When Prompt first gained popularity, it already hinted at its potentially short lifespan, but perhaps no one expected its decline to come so quickly. The backend debugging interface for GPTs is largely based on a natural language model in English.
The ability to ask reasonable questions, follow the logical derivation of those questions, and ultimately find solutions is becoming increasingly valuable.
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