Generative AI has come a long way in an incredibly short amount of time. It’s transformed from a novelty into a fully-fledged tool that makes life easier for those with limited resources. Generative AI can create text, images, audio, and even video based on your specifications, providing a lifeline for small businesses needing content and marketing materials.

With generative AI being so new, there’s quite a learning curve for those getting started. Within this guide, we’re going to look at just what generative AI is, how it works, how you can use it, and how to navigate its various challenges. 

What is generative AI?

Put simply, generative AI is a type of artificial intelligence that essentially acts like a digital artist that can make all kinds of content on demand. Despite its very recent rise in popularity, generative AI is not a new idea. In fact, the first chatbot was created by MIT Professor Joseph Weizenbaum in 1966! 

Things really started to heat up in 2014, when research scientist Ian Goodfellow developed generative adversarial networks (GANs). This technology pits two neural networks against each other, resulting in incredibly realistic content. From this moment on, generative AI stopped being a pipe dream and started being a serious tool for rapid content generation. Let’s take a quick look at what it can do.


Generative AI models like GPT-3 and its successors can generate coherent, contextually relevant text. For instance, they can write articles, compose poetry, or even create code snippets. Here’s an example of a generated text using the prompt “Write a short sentence describing a sunset”:

“As the sun dipped below the rugged peaks, it bathed the serene lake in a gentle, golden radiance.”

That’s great stuff, and you would be hard-pushed to think it wasn’t written by a human.


GANs have resulted in AI image generation becoming incredibly realistic. GANs combined with machine learning and other new technologies have made AI image generation simple with dazzling results. In some cases, even an AI-generated photograph can be indistinguishable from a real-life photograph.   


Ever heard a voice from a computer or a voice assistant that sounds almost like a real person? That’s thanks to AI too. AI voice generation has also improved exponentially in the past couple of years. In many cases, AI voices can recreate a human voice to an indistinguishable level. 


AI video generation is still finding its feet. Some platforms are able to create realistic video content, others will provide video with that signature generative feel. Video editing with AI however can work very well, using pre-captured video and editing it together to create engaging video content. As AI image generation continues to improve, we expect AI video generation will quickly follow in its footsteps.

How does generative AI work?

Generative AI is often seen as the successor to traditional AI, but they’re actually just two different approaches to artificial intelligence.  

Traditional AI, often referred to as rule-based or classical AI, is an older paradigm of artificial intelligence that relies on explicitly programmed rules and logic to perform specific tasks. In traditional AI systems, human experts create rules and algorithms that will process data and make decisions. They’re purpose-built to serve specific situations and do not possess the ability to learn from data or adapt autonomously.

On the other hand, generative AI is a more modern and advanced approach that falls under the umbrella of machine learning. Generative AI systems, such as generative neural networks, use large datasets to learn patterns and generate content autonomously. These systems can create new, creative content, whether it’s text, images, audio, or even videos, without explicit programming or rule-based instructions. 

Machine learning

Until recent years, machine learning was used in a limited capacity. It would be trained to recognize and classify patterns within the content it was given. For example, the insurance industry uses machine learning to recognize common signs of potential fraud by feeding data from previous cases. The insurers could then use their newly trained AI to quickly check over the details of new cases and compare them against thousands of other cases to see how they lined up. 

Machine learning has made generative AI into a powerful tool through self-supervised learning. This type of machine learning requires a substantial amount of data to be fed into the AI, which it will then use to generate predictions. After all, generative AI is still looking for patterns and predicting the most likely outcome based on your prompt. You could even argue that it’s simply statistics. 

With enough data being fed to an AI, the resulting generated content is simply the most statistically probable outcome. Say you ask ChatGPT a question, the AI will scour its database to identify the most likely answer. This is why information is occasionally incorrect, as it relies on pre-fed data rather than looking through the entire internet for the most up-to-date information. The more data fed into an AI, the more accurate its predictions can become. 

Prompt engineering

Users of generative AI have been working just as hard as developers in creating ways to improve the output of AI models. Prompt engineering has become the “secret sauce” to making generative AI feel human, to the point where companies are creating new roles and paying $300k salaries.

Prompt engineering itself is quite simple — it tasks you with finding the perfect prompts that will guide the AI towards the correct result. This has wide-ranging applications across all generative AI platforms, with audio, image, text, and video requiring prompts to give you the result you’re looking for. 

Getting prompt engineering right can take time. It’s often an iterative process that requires refinement, and the same prompt can have a different impact depending on the AI platform you’re using. There are, of course, applications that will automate prompts on your behalf. However, much like the AI itself, automated prompt generation is going to give you generic results that may not be exactly what you’re looking for.

Benefits of generative AI

Generative AI has a huge range of benefits for a wide range of industries. It has become a crucial tool for many businesses for the following reasons.

Get the content you need, fast

Even the best content creators can’t deny that AI gets the job done faster. Human creators can spend hours, even days crafting the perfect content for your business. However, we’re not always afforded the luxury of time. Generative AI can create content in minutes, or even seconds when it comes to short-form content. 

Save money and resources

Like time, money is finite. Small businesses especially are not able to allocate huge budgets to content-based marketing. Using AI with a prompt engineer allows a business to create the content they need without putting themselves out of pocket.

Easier ideation

With so much data to pull from, generative AI is an incredibly useful tool when it comes to ideation. Text-based generative AI platforms can bring fresh perspectives to the table to uncover ideas your team may not have imagined. 

You can use generative AI for a number of ideation purposes, including:

  • Concept generation
  • Concept expansion
  • Content inspiration
  • Prototyping and visualization
  • Scenario generation 

On top of this, generative AI can also identify market gaps by showing you what is already being done. This will help you to expand your business’ horizons and enter new areas that your competitors have not touched yet. 

Engaging and informative summaries

Generative AI is already working from a huge collection of data and is constantly learning which parts of that data are most important. This makes it an amazing tool when it comes to summarizing, as it can quickly identify key points within your content and generate summaries that touch upon every single one in your summary.

Make sense of large data sets

Generative AI can generate summaries or representative samples of large datasets. This is particularly helpful for providing quick insights into the dataset’s content without the need to process the entire dataset exhaustively.

It can also be used to generate visual representations of data, such as charts, graphs, or diagrams, to help analysts and researchers gain insights from large datasets more easily.

Speed up existing systems

Generative AI can enhance the efficiency of slow systems by accelerating processes and improving resource utilization through tasks such as data compression, optimization, and predictive modeling. This helps businesses to streamline operations, reduce computational overhead, and enhance overall system performance.

Challenges of generative AI

Generative AI has a significant amount of naysayers. This makes it a tricky topic of conversation, especially for those who make a living creating content and are worried that AI is being introduced to replace creative careers. Let’s look at some of the key issues that come with generative AI.


Generative AI has become a valuable tool for coders looking to automate common, often tedious tasks. The problem here is that text-generation AI platforms now have a huge amount of code on their databases. This can then be used by unscrupulous individuals to create code with malicious intent, even if they don’t know how to write code themselves.

Poor quality and misinformation

Social media has a big misinformation problem right now and there are some who worry generative AI is fueling the fire. Anyone can create large amounts of low-quality, potentially misleading content and post it to the internet without any oversight. This can quickly lead to AI-generated content being presented as fact, with potentially damaging consequences.

We saw this happen very quickly with Meta’s “Galactica” AI. The tech giant trained Galactica using 48 million scientific papers, aiming to help “organize science”. Unfortunately, they had to shut down the AI after just 2 days. Rather than giving factual snippets, the AI would generate nonsensical responses that would contradict itself. In some cases, it couldn’t even answer simple math questions correctly.


One of the biggest issues people are highlighting is how many AI companies are flagrantly ignoring copyright laws. Generative AI models are built by trawling the internet and grabbing content for its database. This is a fully automated process that doesn’t leave much room for copyright compliance. 

While it can be incredibly difficult to see just what copyrighted content has been taken by generative AI platforms, a landmark ruling in the US has set a precedent when it comes to copyrighting AI-generated work. A piece of art was rejected by the US Copyright Office multiple times, leading Stephen Thaler to seek help from the US District Court. In the ruling, Judge Beryl A. Howell stated that the AI-generated piece could not be copyrighted due to the lack of “human authorship”. 

Generative AI big players

Let’s wrap up by quickly looking at the key names in the generative AI market right now.

Open AI

Open AI is really blazing the trail for generative AI. You can’t talk about text generation without mentioning ChatGPT, and DALL-E was at the forefront of AI image generation. They currently underpin safety and responsibility in all new developments, with the aim of aligning generative AI with human values.

Meta AI

It will come as little surprise to learn that Meta is championing AI. They’ve recently launched Llama 2, a free and open-source large language model that can be used for research and commercial use.


Google has been working on AI platforms for a long time now with DeepMind and BERT. They are currently focused on responsible innovation and technologies that will make a positive impact on a large scale. 


When we built Genny, we knew businesses were tired of hopping between different platforms to get everything they needed. We knew we had to build the ultimate generative AI tool that combined audio, image, text, and video into one platform. By doing so, we have empowered businesses and institutions across the globe with the tools to create engaging content. 

Genny’s speciality is AI voice generation. Our text-to-speech is simply magical, with over 100 languages, 30 different emotions, and a huge range of ultra-realistic AI voices which even includes Santa Claus!

The best part? You can get started for free right now. We can’t wait to see what you come up with!