Key takeaways
Generative AI is a type of artificial intelligence that generates images, text, videos, and other media in response to inputted prompts from a user.
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AI generators, like ChatGPT and DALL-E, have become popular for their ability to handle time-consuming tasks.
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Organizations across all sectors use GenAI as a tool to help with tasks like research, coding, design, and more.
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You can build a career around generative AI in roles like AI trainer, AI implementation specialist, AI content strategist, or AI compliance manager.
Learn about the definition of GenAI, how it differs from traditional AI, and the benefits and limitations of this new technology. Afterward, build your knowledge of GenAI with Vanderbilt University’s Prompt Engineering Specialization. This three-course beginner-level series takes an average of four weeks to complete and can help you build skills like AI enablement, using generative AI as a tool to help you, and how to get more from your interactions with large language models.
What is generative AI?
Generative AI, commonly called GenAI, allows users to input a variety of prompts to generate new content, such as text, images, videos, sounds, code, 3D designs, and other media. It is trained on documents and artifacts that already exist online, “learning” from these data sets so it can predict outcomes in the same ways humans might create on their own.
The rise of generative AI is largely due to the fact that people can use natural language to prompt AI now, so the use cases for it have multiplied. Across different industries, AI generators are now being used as a companion for writing, research, coding, designing, and more. And GenAI will continue to evolve as it’s trained on more data.
What’s the difference between AI and generative AI?
Generative AI is a subset of artificial intelligence, which is essentially defined as the pursuit of creating machines capable of exhibiting (or exceeding) human intelligence. GenAI is a type of machine learning focused on building generative models capable of producing a wide range of AI-generated content, including human-like text, images, and audio. So while AI is typically designed to perform a narrow range of tasks repetitively, GenAI can produce original content in response to various user inputs.
Learn more: AI vs. Generative AI: The Differences Explained.
What is the main goal of generative AI, and how does it work?
Generative AI models generate new content by using neural networks to identify patterns in existing data. Trained on unsupervised and semi-supervised learning approaches, organizations can create foundation models from large, unlabeled data sets, essentially forming a base for AI systems to perform tasks.
There are many generative AI models, including large language models (like ChatGPT), image generation models (like DALL-E), and audio generation models.
Popular AI generators
There are several generative AI platforms you can become familiar with. You may find them helpful for automating certain processes in your workflow.
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ChatGPT: This language model has a foundation of GPT architecture that generates text that resembles something a human would produce. It’s a helpful companion for research, strategy, and content creation.
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Microsoft Copilot: This model, based on ChatGPT, is integrated into Microsoft’s popular productivity suite of applications and is also available as a stand-alone product offering.
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Google Gemini: Google’s answer to ChatGPT, Gemini is integrated into Google’s own suite of productivity applications but is also accessible as a stand-alone chatbot.
Generative AI examples
Use the examples below to understand the different ways you can use GenAI.
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Outlining briefs, resumes, term papers, and more
Generative AI has many use cases that can benefit the way we work, by speeding up the content creation process or reducing the effort put into crafting an initial outline for a survey or email. However, generative AI also has limitations that may cause concern if it remains unregulated.
Benefits and limitations of generative AI
As with any new technology, generative AI has benefits and limitations. For instance, while it can work across multiple languages, making it useful globally, it can also “hallucinate” answers and provide incorrect information. It’s therefore important to understand AI ethics.
Let’s review some of the other benefits and limitations to keep in mind as you use GenAI in your work.
Benefits of GenAI
Some of the benefits of generative AI include:
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Multiple use cases: GenAI can be used in several different situations, such as for coding or writing a resume.
Limitations of GenAI
Some of the limitations of generative AI include:
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Requires human oversight and fact-checking: Because GenAI can make mistakes, lacks a true understanding, and does not have genuine empathy, it requires humans to review its outputs.
Working in AI: GenAI opportunities
While GenAI is a technology that many professionals will need to learn to work with as more companies require the AI skills needed to do so, there are specific roles for those interested in working in AI. These opportunities are limited to highly technical programmers or engineers, either. There are entry-level AI jobs to explore as well as more advanced jobs.
If you’d like to explore a career in GenAI, consider the following job titles:
Technical GenAI roles:
Business GenAI roles
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AI product manager
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AI implementation specialist
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AI solutions architect
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AI business analyst
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AI ethics officer
Creative GenAI roles
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AI content strategist
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AI writing specialist
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AI content editor
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AI UX designer
Support GenAI roles
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AI support specialist
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AI training coordinator
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AI quality assurance specialist
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AI compliance manager
Explore our free GenAI resources to continue building in-demand skills
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