Introduction
AI has become so commonplace in our modern day, that these days most people don’t spare a second thought about it. It exists everywhere, from our work applications (like GitHub), to our search engines (Google’s AI-Powered Search), even down to our home appliances (modern smart fridges employing AI software to employ the ideal amount of refrigeration for your foods).
One can not help but wonder though, what exactly is AI? Now you could just Google that of course, but more likely than not you’ll find yourself scanning articles full of technical jargon that is otherwise illegible.
Well, good thing we’re here to break it down for you! We’ve been working with AI applications for a few years now and we’re proud to say we know a thing or two. So if you find yourself in need of a helping hand to set up your own AI bot, or simply to understand how such a function may help your business, feel free to reach out and we’ll answer your questions as best we can.
The first thing you have to know is that there’s more than one kind of AI. The AI we generally see around is little more than a complex predictive algorithm, generating words and answers by pulling information from online and deriving the next steps; The next word in a sentence, the next function to complete, etc.
Generative AI is one such AI algorithm, and will be the main topic of our discussion today.
What exactly is Generative AI?
Generative AI refers to AI algorithms that focus on creating “content”, content such as images, videos, explanations, essays, and more. The best examples of Generative AI algorithms are bots like ChatGPT, Dall-E, Sora, DeepSeek, and more.
These algorithms are often given access to a database of information and functions, and their purpose is often to follow the provided prompt within their next answer as closely to the letter as they can.
Ask it a historical question?
The AI will provide a singular answer to give you the most accurate information possible, which you can then edit by providing further prompts. The same goes for image and video, after being provided a prompt, software like Sora and Dall-E will attempt to “create” an image or video based on their databases of media, calling on content tagged with topics similar to the provided prompt.
What sets Generative AI apart from the simple predictive algorithms of old though?
Well, AI models based on those older algorithms were static, simply pulling data and using that to predict things like the next word in the sentence.
Gen AI, on the other hand, utilizes a self-improving algorithm, since it attempts to craft “original content”, meaning that the answer or media it provides back to you is meant to be unique, different from the files in its database or throughout the history of its use. In other words, it is not only original, but it is adaptive.
That being said, what then separates it from more modern models, such as Agentic AI?
Generative VS Agentic AI, What is the Difference?
The main difference between Generative and Agentic AI are their functions;
Gen AI is used almost entirely for content creation, that means the creation of word-or-media based content, essays, pictures and videos, code, things of such nature. They are unable to complete complex processes by themselves, or adapt to situations without someone explaining the context of the situation to them.
Ag AI conversely, is used specifically for complex processes, and is prized for its ability to analyze and adapt to the contexts it’s in. Agentic AI is mostly used in software for Virtual Assistant applications (scheduling meetings, transcription, automating emails), or in cases such as healthcare and manufacturing.
The main selling point of Agentic AI when it’s compared to Generative AI, is that the former has the ability to function autonomously, after providing it with a process, parameters, and data to reference. It can then complete complex processes, and adapt those processes based on the situations it encounters.
Within healthcare this can mean monitoring patient conditions, aiding in drug discovery, and more, while in manufacturing AI models like these can be used to automate simple manufacturing processes, or monitor the health of the equipment and flag them for maintenance as needed.
What kind of products use Generative AI these days?
Considering Gen AI’s limitations as seen above, what kind of products do we tend to see Gen AI in? Well, a few cases we’ve already discussed, such as Sora, Dall-E or ChatGPT, each one a form of “chatbot” that you can provide prompts to and receive a singular response to.
In fact, Generative AI is much more common than one would expect. Even the support bots on major sites these days, or automated email response bots, are forms of Gen AI. Also if you’re an average user of social applications like Discord, many of the bots you can add to your servers are AI with the ability to generate completely new images or videos, not just fetch them.
We’ve even developed some Gen AI projects ourselves, including providing it with a database of information, training it as needed, and focusing it to the client’s particular niche. An example being “Hey Smarty”, an AI-application we created for XYZies that would enable users to ask questions regarding home-security, specifically regarding the field of tech, such as internet security and service plans, as well as their in-built home-security system.
We learned so much from the project we even wrote a whole case study on it, about the issues we faced, how we overcame them, and how we managed to provide such a polished deliverable at the end of the day. Seeing this as a learning opportunity not only allowed us to mark what not to do for the future, but also sharpened the skills of our developers to a point, especially when it came to Gen AI projects in the future.
But I digress, the point of these examples is to portray not only the use cases of Gen AI, but the limits it currently faces. While it can not complete complex, drawn-out processes, it does much more than just meet the mark in content creation. The flip-side of this though is the resource use. The more complex the content you task a Generative AI to create, the greater the amount of resources it has to allocate.
This process has been streamlined since its inception, so much so that companies like ChatGPT, or DeepSeek, allow word-based tasks to be carried out for free! Yet image and video generation are still resource heavy tasks and so most, if not all, services that provide such capabilities are paid either through monthly subscriptions or credits.
So, knowing this, what may be the next step for Gen AI?
What are the next steps for Gen AI?
Generative AI has slowly begun to move out of the world of purely content. We have begun to see the implementation of Gen AI function for coders, and not lazy implementation but rather efficient. A great example of this is the GitHub Copilot.
GitHub Copilot is an addon function for developers that automatically analyze their code, makes suggestions, and upon the request of the developer, even have the ability to rewrite code making it cleaner/more legible and more efficient. Even our own developers have begun using Copilot for in-house and client projects, claiming that it has indeed been supremely helpful and cut the time it takes them to code in specific-functions by a substantial margin.
While it does take some getting used to, the success of Copilot points to a very lucrative future for Gen AI. This form of LLM has the ability to speed up workflow and cut project times down, especially considering that, more often than not, development can take the most time. In short, Gen AI has the potential to transform the field of development completely.
It is also important for us to pay attention to the slowly reducing resource cost these models take as their functions are further streamlined. Imagine a world where everyone has access to a top-of-the-line AI virtual assistant from the get-go, with functions we still only see from paid or business-minded versions of the application, all for free.
The very reason businesses are often the first to get their hands on advanced versions of Gen AI is because they have deeper pockets, and developers have to meet operating and project costs. But if these tasks continue to take decreasing resources, we may see a world where such functions are released on Day 1 to the public, and for either free or a price point so low it’s negligible.
Conclusion
So, what do we now know about Generative AI?
Well, firstly, we know what the purpose of Gen AI is now. Generative AI, understandably, generates content. This content can be based in media or language, allowing generated content to range from essays to lines of code, to custom images and videos. This allows Gen AI to be used in chatbot applications (like ChatGPT), media generation bots (like Dall-E), and even coding assistants (like Copilot).
Besides its purpose and use cases, we also now know the difference between the models and purposes of Gen AI as compared to other forms of AI, old and new. What it can do, and consequently, what its limitations are. Such as its inability to complete complex processes.
Finally, we’ve also discussed what the next prospects for Gen AI are, how it may improve, and what kind of functions we may see from it in the now not so distant future.
We hope that this article helped you understand a bit more what people may mean when they refer to Generative AI. The types of AI, their purposes, and how they work can be complex topics, and the more you know about them the better you can figure out which ones can help your business, and how.
If you’re still confused though and need some guidance, worry not. Feel free to reach out to us here at Genetech Solutions, our AI team is more than willing to help you navigate the complicated world of AI, and how it can help your business!
