What Is Generative AI? How It Works, Examples, Benefits, and Limitations

Generative AI is a type of artificial intelligence tool that generates images, text, videos, and other media in response to inputted prompts from a user. AI generators, like ChatGPT and DALL-E, have become popular for their ability to handle time-consuming tasks.

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 DeepLearning.AI's Generative AI for Everyone course.

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 about the difference between AI and generative AI.

How does generative AI work?

Generative AI models generates 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 [1].

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.

• 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.

• DALL-E3: This model generates images from text prompts, so creatives can create vibrant illustrations and concept art that’s a useful accompaniment to content marketing. 

• 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. 

• 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.

• Writing or improving content by producing a draft text in a specific style or length 

• Adding subtitles or dubbing educational content, films, and other content in different languages

• Outlining briefs, resumes, term papers, and more

• Receiving a generic code to edit or improve upon

• Summarizing articles, emails, and reports

• Improving demonstration or explanation videos

• Creating music in a specific tone or style

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:

• Speed: GenAI tools work quickly to complete tasks and generate content.

• Multiple use cases: GenAI can be used in several different situations, such as for coding or writing a resume.

• Scalable: GenAI tools are often equipped to handle multiple users and tasks without degrading performance.

Limitations of GenAI

Some of the limitations of generative AI include:

• 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.

• Bias in training data: AI systems learn from existing human-created content. If that content contains societal biases or prejudices, the AI can absorb and repeat them.

• Privacy concerns: There can be security risks. AI systems store user interactions and inputs, and sensitive personal or business information may be shared during conversations.

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:

• Machine learning engineer

• AI trainer

• AI architect

• AI data scientist

Business GenAI roles:

1. AI product manager

2. AI implementation specialist

3. AI solutions architect

4. AI business analyst

5. AI ethics officer

Creative GenAI roles:

1. AI content strategist

2. AI writing specialist

3. AI content editor

4. AI UX designer

Support GenAI roles:

1. AI support specialist

2. AI training coordinator

3. AI quality assurance specialist

4. AI compliance manager

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