What Is DALL-E?

Written by Coursera Staff • Updated on

Learn about the generative AI DALL-E, how it works, and what you can use it for.

[Featured image] A man showing his coworker concept art generated on DALL-E.

DALL-E is an artificial intelligence model that can generate images when you feed it textual descriptions. To accomplish this, it translates billions of text bits from the internet into an abstraction of stored information, which it then uses as a reference tool of describable things for generating those images. First introduced in 2021, DALL-E has seen continuous updates as it has become increasingly popular. DALL-E learns a “latent space representation of images,” allowing it to generate high-quality and varied images [1]. This article describes the uses of DALL-E, who uses it, the pros and cons of this technology, and how you can start using it.

How DALL-E works

Inspired by the human brain, DALL-E attempts to mimic the creative process human artists experience while creating their own work. DALL-E’s use of connections between subjects allows it to make associations, which it can then use to generate artwork, and this process reflects the one your brain employs to produce your thought patterns. Trained on an extensive data set of image-to-text pairs, DALL-E can run your text input through a text encoder and generate an image based on the information received from its image decoder.

What is DALL-E used for?

DALL-E has a wide variety of uses, including the following:

  • Brai25nstorming ideas

  • Custom printed art

  • Creating 3D art

  • Creating 3D renders

  • Marketing visuals

  • Designing logos and brand materials

  • Creating educational visual aids

Brainstorming and custom art

You can use DALL-E to generate concept art and design elements based on the text you input, speeding up the design process. You can also use DALL-E to spruce up your business's or restaurant's interior design by generating art pieces, printing them out, and decorating with them. As a 3D artist, you can use DALL-E to mock up 3D renders before proceeding with 3D modeling, saving you brainstorming time.

Marketing and brand materials

You can use DALL-E to generate relevant images for ad campaigns. You might consider providing the AI model with your target audiences and a detailed product description to curate DALL-E's content further. You can also use DALL-E to put your advertising product in a traditionally difficult-to-achieve background—like a mountain or coral reef. DALL-E generates visibly interesting brand imagery with a uniform style.

Creating educational visual aids

As an instructor, you can use DALL-E to generate images representing difficult-to-understand subjects for your learners. For learners who absorb material visually, DALL-E can create visual aids for educational use to complement your teaching style, like representations of different organizational structures. The material generated can allow teachers, instructors, trainers, and professors to teach more successfully, empowering learners to commit the material to memory more effectively.

Who uses DALL-E?

Various careers and individuals utilize DALL-E, including creative minds looking to have fun, sellers creating prints of the AI artwork, and businesses for productivity benefits. Given that AI systems and their uses are still growing, more careers related to DALL-E will be available to you in the future. As a machine learning engineer, an AI engineer, or a computer vision engineer, you may also use generative AI like DALL-E. Additionally, if you’re interested in one of these positions, the US Bureau of Labor Statistics states that this sector of the economy expects a 23 percent growth rate from 2022 to 2023, which is much faster than average [2]. You can read below in more detail what machine learning, artificial intelligence, and computer vision engineers do. 

Machine learning engineer

As a machine learning (ML) engineer, you create and solve issues related to technology-based applications, programs, and devices. An ML engineer combines programming and data science principles to form a multidisciplinary role with specialization and conventional software development. Since machine learning is one of the various practices used to create artificial intelligence and AI tools, you, as an ML engineer, would design, test, and assemble AI systems grounded in machine learning principles.

Machine learning engineer: $119,011, average annual salary [3]

AI engineer

AI engineering involves the development of tools, systems, and processes to permit the usage of artificial intelligence in the real world. As an AI engineer, you program with Java, C++, or Python, work with data, apply machine learning algorithms and libraries, and research and design deep learning applications. AI engineers use the programs they write and the data they collect to create AI tools like DALL-E.

AI engineer: $119,011, average annual salary [4]

Computer vision engineer

If you become a computer vision engineer, you will instruct computers to process, understand, and recognize images. This technology is already being utilized in many applications, such as facial recognition, image enhancement, content moderation, and image search. You will need a strong working knowledge of computer programming languages like Python and Java in this position. Your everyday duties might include developing and testing algorithms, presenting novel solutions to real-world problems, or managing computer vision projects of various sizes.

Computer vision engineer: $109,201, average annual salary [5]

Pros and cons of using DALL-E

Due to the open-source nature of generative AI like DALL-E, you can find both pros and cons with its usage.


  • High quality: The content you generate with DALL-E is very good quality and correctly corresponds to textual inputs, providing your creative industry with a crucial new tool.

  • Versatility: With DALL-E you can generate incredibly unique and specific visuals, from realistic to fantastical, making it very versatile.

  • Real-time applications: With the evolution of technology using generative AI and the ever-changing AI itself, real-time applications of AI like DALL-E are likely to become more common, maybe when you are editing video or creating content.


  • Trouble generating text: DALL-E—and even the newest model DALL-E 3—has difficulty properly generating text within its images. If you wish to avoid this issue, describe your image in greater detail, leaving out mention of text.

  • Job displacement: Usage of DALL-E can contribute to the displacement of creative-based jobs, as you may have the AI model perform a task that would previously require hiring an artist or graphic designer.

  • Ethical concerns: Unfortunately, generative AI tools like DALL-E come with ethical concerns, including deepfakes, bias, and the automation of jobs, which can negatively affect people’s ability to earn money. 

How to get started with DALL-E

You can begin using DALL-E to see how it responds to your text inputs. Visit the OpenAI website and either login or make an account with OpenAI, then begin describing the image you would like to create and click “generate.” DALL-E will then create four different images based on your text input.

Prompt engineering

You can use prompt engineering to design text input prompts for DALL-E to attain your desired product. You can think about details like clarity, specificity, and context when creating practical and effective prompts. This will provide the AI with enough information to generate an applicable response. Consider using specific methods, like the rhetorical approach, which has you describe your primary claim and then your rhetorical situation, such as the audience, context, and style/delivery.

Learn more with Coursera.

If you’re looking to learn more about generative AI and how to use it, consider trying the Generative AI with Large Language Models course from AWS and DeepLearning.AI on Coursera to further your understanding of how to create AI models. Or, you can expand your knowledge of using generative AI to enhance your daily life with the Generative AI Fundamentals Specialization from IBM, also on Coursera.

Article sources


University of Michigan. “GenAI In-Depth: The Science and Capabilities of GenAI, https://genai.umich.edu/in-depth/science-and-capabilities.” Accessed March 14, 2024.

Keep reading

Updated on
Written by:

Editorial Team

Coursera’s editorial team is comprised of highly experienced professional editors, writers, and fact...

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.