Mind Readings: Why I Use Developer Generative AI Tools

In today's episode, let's talk about which AI tools uh to use, or more accurately, which AI tools I use, because I use very different tools than a lot of other folks do. Um let me show you. I'm gonna show you a couple different examples here. Let me go ahead and share my screen. If you're listening to this, uh, you'll want to head over to the YouTube channel.

Um, I'm gonna pull up the consumer interface to Google Gemini, and I'm gonna pull up the developer interface to Google Gemini. These two things are both part of the Gemini ecosystem. However, they're very, very different. So the consumer interface, which is we're looking at here, has Gemini, it has a prompt, it has a warning that hey, human beings will review this, etc. And you put your prompts in, and it just goes and it does what you want it to do.

The developer interface within AI Studio has a lot more stuff. So there are there's a model selector here, which lets you choose which version of Gemini you want to use. I'm currently using 1.5 Pro. It has a temperature setting, which allows you to tell the model, hey, be less creative or more creative. It has safety settings that you can turn up or down, and even advanced settings like say, like, hey, I want you to write code or I want you to return your responses in a different format, and you can change things like the stopping sequence.

Why would you use this tool that's way more complicated and has more buttons and more stuff than this tool, which is really simple, just add the prompt, add your documents, and go. For someone like me, who is using these tools to try and get specific, repeatable, reliable results. The consumer interface, which you see here, ain't it? And here's why. Underneath the hood in systems like Google Gemini, the consumer version, Chat GPT, Anthropic Cloud, there's the model which does all the work, takes your prompt and processes and puts out a result.

However, before there, there is a on the consumer interfaces, there is a router. There's a piece of software that looks at the prompt and says, where should we send this? How can we how can we most efficiently help this user out? You will notice with Google's uh developer interface here, there's five different models available. There is Gemini 1.0 Pro, which is the old version, so 1.5 Pro, which is the new version.

There's Flash, which is a faster but kind of dumber model that but it's really, really fast. And there's two open source models here, Gemma 9 uh and Gemma 27. When you use the consumer version, there's a router that says, well, what kind of query is this? Is it something simple? If so, let's route it to a less expensive computationally expensive model.

Let's route it to 1.5 Flash rather than Pro, because Pro consumes a lot of resources, it's very heavy, it's slower, but it's way more accurate and way more thoughtful and way more capable than Flash. When you're using a consumer interface, you don't get that choice. You are automatically routed by its best decision, but best uh assumptions where your query should go. How which model should process your prompt. When you're using the developer version, you decide what model you want to use because you're paying per use, right?

When you're using the developer edition, you're paying um per invocation of the model. Whereas with the consumer interface, you're paying like 20 bucks a month. AI companies have incentives to run as cheaply as possible. So they will route your prompts to the cheapest model possible. A, it's faster for you, the user, so it's theoretically a better experience from a speed perspective, but they're going to route it to the lowest quality model because low quality models are faster.

Whereas if you need a specific level of precision or you need a specific set of capabilities, use the developer version and you pay more, but you are focused then on the specific model that you want to use, because presumably, at least that's what all the companies are hoping for. You will build an app on top of that specific model. So I personally, and this is just a me thing, um, I prefer to have that level of control. I like to have the level of control where I can say, you know what? For the work that I'm doing, I'm okay turning off the safeties and saying, let's let's go wild, let's let the model do what it wants to do.

Um, and sometimes you will see when you're running in the developer mode, it will flag like, hey, this we're flagging this output here could be dangerous content. We're still producing it, but we're telling you, maybe don't if you're building an app on this, don't show this to the general public because it could be offensive. Whereas if you're in the consumer model, it will just say, Nope, can't do that. Sorry, and you don't know why, and you don't have any control over changing that. I like to think of these things as kind of like uh the consumer model is the one that's user-friendly and has lots of guardrails.

It's like when you're at the bowling alley and they inflate those little bumpers to fill the gutters and say, okay, well, you know what? Now you can only bowl in a certain area here. And for most people, that's okay. For most people, most time with most use cases, that's okay. But there is an incentive, there's a profit incentive for AI companies to route you to the cheapest possible model, the lowest performing uh cheap model.

As opposed to if you want a very if you want to know what's going on under the hood, if you want control over which model is going to be processing your prompts, use the developer interfaces. That's gonna do it for this episode. Thanks for tuning in. We'll talk to you soon. If you enjoyed this video, please hit the like button.

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