What's the Difference Between GPT and ChatGPT Projects?

Ever felt like you're constantly re-explaining your project to your AI chat? I know I have. Jumping into a new chat, having to re-upload your website, or your files, and then copy-pasting your code again just gets old real fast. It's a real productivity killer.

So, let's break down the difference between GPT and ChatGPT projects, based on what I've learned over time and the pain points I've experienced firsthand.

Core Differences That Matter

In my experience, here's what I've found to be the most crucial distinctions:

• Model Foundation: GPT (Generative Pre-trained Transformer) is the underlying architecture and technology. Think of it as the engine. ChatGPT is a specific implementation and application built on top of GPT – it's the car that uses the engine.
• Specific Training Data: ChatGPT is trained with a specific fine-tuning process, primarily focused on conversational abilities. GPT models can be fine-tuned for many different tasks, which makes ChatGPT is optimized for dialogue and back-and-forth conversations.
• Interaction Style: I've noticed a difference in how each handles interactions. Using GPT directly often involves more explicit instructions. With ChatGPT, the training allows the model to understand conversational nuances, like following up on previous inputs or quickly understanding context.
• Project setup: I've found that it is more efficient to set up projects with your websites, files, GitHub repos in the same way. Starting a new chat that already knows your data has been a game changer

Practical Applications & Real-World Scenarios

Let me give you an example – working on a new product with many moving parts. I might start with GPT to quickly get the initial code templates, focusing primarily on accuracy within a particular domain of coding. As I get to the point of making it “human-like”, I would use ChatGPT to refine its explanations and user interaction, since it already has some base knowledge about how to do this.

But the true test is in the workflow. Repeatedly feeding the same information into chats is a distraction from your core work. That’s where it gets really frustrating, right?

Leveraging Better Tools

I've noticed that the best results come from using the right tools and the right way to build them. It sounds simple, but it makes a world of difference. Sometimes the process can be cumbersome, when you are switching chat windows over and over again.

What I recommend is setting up each of your projects to know your data beforehand. So you can use the right AI chat every time. And no, I'm not only talking about ChatGPT or GPT. There are many models you could use. You want to have a way to be able to switch between models easily and efficiently.

That’s when I really started seeing the value of having your projects ready to go. I wish I'd had this years ago

Introducing Contextch.at

Speaking of saving time, the tools I’ve been using have really helped me boost productivity. It's called Contextch.at. It helps in the same way I mentioned before, so it starts to feel natural. You can create projects with your websites, your files, even your GitHub repos. This way, you can start new chats that already know your data. You start using it and you just feel like it really makes sense.

And the features, like selectable AI models, context builder, a cost calculator, are pretty useful. I really appreciate it, and it's pay-per-use, no subscription fees! It is a small thing, but it helps a lot. Highly recommend it if you're looking for this kind of solution.