Artificial intelligence (AI) art generators and AI art generators are getting a lot of buzz right now. But they're also generating a fair share of controversy, which isn't a surprise. Just ask Gutenberg—new tech and big concerns go waaay back together. 

It's unwise for the pro-tech crowd to dismiss all concerns as hand-wringing by the status quo. As we push AI systems to become faster, smarter, and cheaper to use, it does get easier for bad actors to steal or take credit for creative work and intellectual property (IP). 

But at their best, AI art generators are revolutionizing the ability of legitimate content creators to produce high-quality, original art and imagery. For many, the question isn’t whether or not AI art generators can improve their creative process, but which one to try first.

If AI image creation has caught your eye, read on for some pointers that will set you down the road for success. In doing so, we'll cover why the need for artwork is a big deal in content marketing, social media, and web design worlds. We'll do a basic breakdown of how AI art tools work their magic. And finally, we focus on the skillset needed to get that magic working for you.

AI generated art vs. stock photography

Data-backed evidence has proved again and again that image-rich content is preferable online. 

So, as the need for artwork in content grew, stock image companies pumped-up the jam to keep up with demand. Unfortunately, available stock quality lagged behind quantity.

Source: Marvin Meyer via Unsplash.com

Or it did, until Unsplash.com pioneered a model of stock photography websites that was without copyrights, insanely high-quality, and, in many cases, 100% free. 

Suddenly, thanks to Unsplash (and all the sites inspired to follow this new quality-over-copyright way), it became easy for anyone and everyone to find beautiful, high-resolution art and imagery online. 

Sadly, the democratization of high-quality stock quickly became a liability. Especially for content creators working to create truly unique, high-performing content. Everyone suddenly had access to a limited number of the exact same images—and made use of them.

Source: Google Image Search

Navigating this endless whipsawing between the availability of art and the quality of art that’s available takes time and energy away from actually getting the work done. 

That’s why, for many creators, AI art generators have so much potential. AI images, after all, are accessible and one-of-a-kind. But tapping into AI potential requires understanding the basics of how AI marketing tools work. 

How machine learning transforms AI into art tools

There’s a bucketload of terms, abbreviations, and phrases used in the world of AI image generators, and AI creation tools in general. But there are three main concepts that will improve your ability to work with AI to create unique, usable images. 

These key concepts are: algorithms, machine/deep learning, and GPT-3 (Generative Pre-trained Transformer 3).

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1. Algorithms and AI

How do machines solve problems? 

At their core, algorithms are simply rules that computers follow in order to solve problems or perform calculations. Think of them as automatic instructions. 

For example, algorithms are how marketing platforms decide which banner ads to serve you based on your browsing behavior:

If user clicks [X], show user [Y].

In our case, algorithms are what guide art generators from input to output. But the process at play is more complex than any single algorithm can handle. 

Enter artificial intelligence, a way of describing bundles of algorithms that work together to perform more complicated tasks—like erasing your ex from all of your vacation photos. 

AI is already doing things that border on the unimaginable. But remember, the rules are the rules. 

One of the challenges of working with AI tools is that the user must learn how to play by a given set of rules to get what they want. 

But, as it turns out, AI tools are doing a lot of learning too. 

2. Machine learning

The term “machine learning” (ML) was coined in the 1950s by AI pioneer Arthur Samuel. This term is often used interchangeably with AI, but to be more accurate, machine learning is a process by which AI can improve its performance. The AI does this on its own, over time, by building feedback loops into its own system. 

Remember our simple “if X then Y” algorithm for the banner ad? Taking this a step further, the machine can learn to change its behavior based on user interactions. Here’s a simple example: 

If user clicks [X], show user [Y]. 

• Did user then click [Y]?

• [YES] = show user more [Y] the next time they click [X]. 
• [NO] = show user [Z] the next time they click [X].

Machine learning can partially or entirely remove the need for human intervention as AI models train on a set of inputs. And, just like their human counterparts, the more information and time a machine gets to train, the better it can become. But the most valuable training feedback still comes from humans—who aim to put this learning to work.

This is why most, if not all, AI art generation tools prompt users to weigh in on how they think the tool is doing as it works to meet their needs. 

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3. GPT-3

So how does the AI have any idea what its users are saying?

Algorithms and machine learning cover quite a bit of what makes AI-generated art possible. But it wouldn't be possible to use these tools at all if machines couldn't comprehend what we were asking them to do. 

Language-related technology, like GPT-3, closes the loop between AI systems and their users.

As the name suggests, GPT-3 is the third generation of OpenAI’s generative pre-trained transformer. This AI language model was trained with 570 gigabytes of text—the equivalent of almost 220,000 e-books—pulled from all over the web. (No wonder conversations with AI can be a little freaky sometimes!)

See related: What is ChatGPT and How is it Different From Jasper Chat?

One major advantage GPT-3 has over its predecessors—and what's helping take art generation tools to the next level—is that it isn't just paying attention to the words we give it. Based on that 570 GB of homework it trained on, GPT-3 also guesses what we'll probably say next. This ability, in turn, helps GPT-3 AI tools like Jasper work out what we mean, including context, intent, and syntax, not just what's literally being said. 

We're only skimming the surface here. There’s much, much more going on with AI tools like Jasper. But understanding these three core concepts, it's easier to appreciate that if I ask an AI art generation tool for an “image that shows a pelican with a giant, oversized bill,” I get options of pelican-ish birds with oversized beaks, and not birds showing us proof that they've been overcharged by their utility companies. 

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Okay, now it’s time to get your creative process in the mix and talk about prompt engineering. And we’ll have a little fun using Jasper to do it. 

AI-generated art tools: Practice makes prompting

Prompt engineering is the info we put into AI tools like Jasper to get the stuff we want out. 

AI art generation is an iterative process. Working with AI requires mindfully constructing inputs, and then learning and improving them based on the results provided. For better, more efficient results, you’ll want to shift your mindset from ordering hamburgers at a fast-food joint to working out recipes in a test kitchen with a fellow chef.

To illustrate the prompt engineering process, let’s say you’re writing an article about data pipelines. 

Here’s what we get when we give Jasper a simple prompt:

“A pipeline” 

Unlike some AI art generation tools that return only one image, Jasper provides multiple results: 

In this case, we prompted Jasper for a pipeline, and that’s exactly what we got. 

Now let’s be a bit more specific with our prompted object and compare the results:

“A data processing pipeline”

The way human brains work, pipelines might have remained a central aspect of the new set of images. Because, to us, pipes and pipelines are common, tangible objects. But for AI art generators, seemingly minor differences in prompt wording can produce wildly different results. 

Here we’ve already hit a major takeaway. Through the prompt engineering process, you are partnering with an AI model to find your way to a solution together. You’ll notice in the second example, the concept has changed but the images don’t make much sense. The more specific details you bring to the table during the process, the better and more efficient your results will be.

Let’s give Jasper a bit more detail using a subject-verb-object format:

“A long metal pipeline delivering digital information to a big computer database”

Here, three swings at the plate later, Jasper is delivering images that are close to something you might want to use in your pipeline article. 

To write a good prompt, it’s important to be both clear and literal. AI models are really good at knowing which words they should pay attention to when prompted, but they’ve still got a lot of learning to do regarding the relationships created between words when humans speak or write. 

To get the most out of AI models like Jasper, it helps to spell things out as precisely as possible. Take the following prompt:

“A goldfish and a bowl of cereal where the goldfish is happy and swimming around”

When you read the prompt above, maybe you had a perfect picture in mind of what the image would look like. But doing so was only possible because you made assumptions and inferences that AI can’t. 

Where is the goldfish swimming, around the outside of the bowl or inside it? Is there milk in the bowl, or is it just full of dry cereal? What kind of cereal is it, warm or cold? Despite this idea being a bit “out there,” we still need to engineer our prompts to be clear and literal. The way we wrote the original prompt, we were inferring it was the bowl of cereal that looked really happy. 

So let’s try to provide Jasper with a clearer prompt for this same image idea:

“A happy goldfish, the goldfish is smiling and swimming around inside of a bowl, the bowl with the goldfish is filled with cheerios and milk”

While the second set of results is no less bizarre than the first (remember, the prompt was bizarre to begin with), we’ve begun to narrow down the results. 

The goldfish are now all inside their respective bowls. Three of the four options involve goldfish that look happy. And, while we’re still lacking any apparent milk, Jasper did a fantastic job in the second image of capturing the look of cheerios.

With all this in mind, the next step is to think not just about what you want to see, but about how you want to see it. 

In addition to saving you time and money, AI art generators free you to explore a wide variety of artistic styles, image formats, compositional formats, and lighting effects. 

Let’s take our happy-fish-in-the-cereal-bowl idea. What if we wanted photorealistic images?

“Photorealisitc, 35mm film, DSLR camera lens, high detail, a happy goldfish, the goldfish is smiling and swimming around inside of a bowl, the bowl with the goldfish is filled with cheerios and milk”

Or what about a fish swimming in a bowl in the style of Rembrandt?

“Painting of a happy goldfish, the goldfish is smiling and swimming around inside of a bowl, the bowl with the goldfish is filled with cheerios and milk, oil painting, oil on panel, in the style of Rembrandt’s The Abduction of Europa”

Note how in these last two examples we’ve pushed the detail provided in the prompts even further. To do so, we’ve dropped some linguistic connective tissue, instead using commas to create small clusters of related information that all ladder up to inform the original idea.

One of the beauties of AI-generated artwork is that it can take you in creative directions you might not have ever imagined. 

The ethics of AI-generated art

Creators are continually inspired by art that isn’t their own. It’s a big part of how art works. But much like creating art by hand, when you use AI to generate digital art, it’s incredibly important to make sure the work you’re generating isn’t just a forgery in disguise.

This is why AI generation tools such as Jasper have been carefully designed to help content creators stay on the right side of commercial copyright concerns. Whether you’re using Jasper to realize surreal, fun fish-and-cheerio themed art or to generate beautiful, photorealistic slices of life, you can rest assured that you won’t be stealing anyone else’s work.

So, which AI art generator should you try first? 

As the pool of content creation tools grows, it’s getting easier to shop around for an AI tool that best fits your creative process.

There are plenty of in-depth lists out there that do a good job of comparing specific features, options, and pricing models. But here are a few of our favorite tools for those who want to start creating AI-generated art:

DALL•E 2

DALL·E 2 is the second text-to-image generator from R&D company OpenAI. Compared to its predecessor, DALL·E 2 generates "more realistic and accurate images with 4x resolution."

Top features:

• Allows users to combine concepts, attributes and styles
• Users can also expand images beyond what’s returned to an initially generated canvas
• Add or remove elements and DALL·E 2 will take shadows, reflections, and textures into account

Pricing:

• Users get 50 free credits during their first month, and an additional 15 free credits per month
• Additional credits can be purchased at any time starting at $15 USD for 115 credits 

Fun fact: DALL·E 2 is actually an important and integral partner to us here at Jasper. They are just one of the technologies that make Jasper Art possible.

Jasper Art

Formerly known as Jarvis, Jasper.ai is a AI-powered writing tool designed for marketing and business writing that also offers a text-to-image art generator.

Top features: 

• Over 50 AI templates
• Over 29 languages supported
• Supports various forms of marketing writing, including long-form content, and conversion-centric content like landing pages and emails

Pricing:

Jasper offers several flexible pricing plans ranging from the $39/mo Creator plan to custom Business plans for an enterprise-level solution.

Midjourney

Midjourney experience takes place almost entirely through Discord, unlike other tools featured here. Often compared to DALL·E 2, some see Midjourney's AI-generated images as "more impressionistic” than OpenAI’s tool. Depending on the needs of a given user, this could be a pro or a con.

Top features:

• Membership options come with friend passes
• As part of the Discord experience, users can see and iterate on art being generated by other users
• Offers control offer image aspect ratios, among other inputs

Pricing:

• Users can create up to 25 images for free
• Basic memberships are $10 USD for 200 images per month
• Standard memberships are $30 USD for unlimited images per month

Nightcafe

NightCafe Studio's goal is to democratize the art making process. It's name is a nod to the company's favorite real life artist, Vincent Van Gogh.

Top features

• A tipping feature allows users to compensate other creators
• Collection creation
• CAD tools
• By subscribing users can enjoy an ad-free experience

Pricing:

• Nightcafe Pro starts at $9.99 USD per month for 200 credits
• Users can also purchase Credit Packs without the need of a subscription starting at $7.99 USD per 100 credits

Ready to push your projects beyond stock photography? (Jasper’s ready to help)

We’ve been thrilled at the response we’ve gotten since adding Jasper Art to our top-rated AI writing tool. And now’s the perfect time to give Jasper Art a try. In addition to the free trial, Jasper Art’s $20 subscription currently gives you access to 100% to all premium features, features that might only be available to specific membership tiers in the future. 

Sign up today to experience other ways Jasper can help you save time while creating higher-quality, better-performing content.