A robot in focus while a professor explains data science in the background to a group of adult learners.

How to Learn to Use Generative AI

Jun 25, 2026, by Staff

In just the last few years, adoption of artificial intelligence (AI) has become so widespread that the terminology used to describe it is often misunderstood. For anyone looking into how to learn generative AI (also called GenAI), it’s important to understand that AI is an umbrella term covering a range of computational approaches that mimic human intelligence. 

Traditional AI performs specific predefined tasks, such as analyzing data or recognizing patterns. By contrast, generative AI can autonomously produce original content based on patterns it has uncovered in large datasets. Learning how to use generative AI is becoming one of the most important things professionals interested in pursuing career advancement can do.

Individuals researching how to learn generative AI will benefit from finding out how the technology works, what it can be used for, and how it can be applied in their current job. It also can be helpful to explore the advantages of earning an advanced degree or certificate in the subject.

Perceptions of Artificial Intelligence

Generative AI adoption has grown quickly since ChatGPT’s launch in 2022, with 55% of people in the United States having used generative AI tools by August 2025, according to the Federal Reserve Bank of St. Louis. AI has become an everyday tool for most Americans, professionally and personally:

  • A 2025 YouGov report on how Americans view AI found that 56% of U.S. adults used AI tools, 28% weekly, with those under 30 more likely to use them than older adults.
  • A 2025 Brookings survey on how Americans are using AI found 57% used generative AI for personal purposes.
  • Generative AI reached 53% population adoption within three years, faster than the PC or the internet, according to Stanford University’s 2026 index report on artificial intelligence.
  • A 2025 Harvard Business Review survey of Gen Z AI users found that 3 out of 4 (74%) U.S. young adults had used an AI chatbot such as ChatGPT or Claude at least once in the past month.

Perceptions of artificial intelligence vary. Many people have concerns about the impact of generative AI on the diversity in new content, while others argue that its use in content creation can enhance individual creativity. And despite misgivings about AI’s impact in areas such as creativity and relationships, a higher percentage of Americans (44%) are more optimistic about AI in medical care than pessimistic (19%), according to the Pew Research Center. 

Understanding the Fundamentals of AI

The history of artificial intelligence reaches back to mid-20th-century electronic computing, when, in 1950, British mathematician Alan Turing began to question whether machines are capable of thinking. AI now is being used across a range of functions, from medical diagnostics and financial forecasting to data analytics. 

For those researching how to learn generative AI, it can be helpful to first place generative AI in the context of other AI model types. The following are examples of other commonly used forms of AI:

  • Traditional (or narrow) AI follows strict rules for specific tasks, such as automating workflows. In contrast, generative AI creates original outputs and improves them with human feedback.
  • Predictive AI forecasts results based on existing data, such as predicting a marketing campaign’s success. Generative AI can go a step further by creating the campaign itself.
  • Conversational AI can understand and respond to natural language, simulating two-way dialogue to answer questions. Generative AI goes beyond that by being able to respond to voice prompts that ask it to create original content, from music to product designs.

Generative AI and Machine Learning

A look at generative AI compared to machine learning (ML) provides further insights into the fundamentals of artificial intelligence. Generative AI is a specialized type of machine learning, which is itself a subset of AI that is focused on algorithms that let systems learn from data and improve over time.

One way to picture machine learning is as a daily commute to a new job. At first, the driver follows their GPS’s directions exactly to avoid getting lost. Over time, though, the more trips the driver takes, the better they learn the traffic patterns and how to work around traffic jams, whether that means taking alternate routes or leaving earlier. Traditional software can’t make that kind of adjustment on its own, but machine learning allows it to learn from past results and make better decisions at scale and with speed.

Before generative AI, most ML models learned from datasets how to classify information or predict outcomes. Generative AI instead creates original content such as text, images, video, audio, or software code in response to a prompt.

How Does Generative AI Work?

Generative AI relies on deep learning models, which are algorithms that mimic how the human brain learns and makes decisions. These models find and encode patterns in huge amounts of data, then use what they’ve learned to interpret requests and respond with new content. Most generative AI works in the following phases:

  • Training builds a foundation model that can support many applications.
  • Tuning adapts that model for a specific use.
  • Generation, evaluation, and returning check the outputs and keep improving their quality.

When prompted, generative AI produces on-the-spot responses that users can refine through further inputs. Other technologies, in addition to ML, make this possible. For example, natural language processing (NLP) turns the nuances of human speech and text into a format computers can work with. In another example, image recognition allows models to interpret and respond to visual input.

Together, these technologies allow generative AI to produce outputs across formats based on patterns learned from vast amounts of data. More diverse datasets tend to yield more precise and creative responses.

Generative AI Architecture

Generative AI models are built on neural network architectures, which are the designs that define how the generative AIs are organized and how information moves through them. Types of architectures include the following:

  • Variational autoencoders (VAEs) compress inputs into a smaller form that keeps the inputs’ most essential features, then rebuild from that compressed version. VAEs are driving breakthroughs in image recognition, NLP, and anomaly detection. VAEs are also playing a key role in drug discoveries.
  • Generative adversarial networks (GANs) pit two models against each other: a generator that creates realistic data and a discriminator that tries to spot the fakes. The goal is for each model to correct the other, sharpening both. While this type of architecture offers useful benefits, GANs can be used for deepfakes, identity theft, and misinformation as well.
  • Transformers power many large language models (LLMs), which are AI systems that model human language using millions or billions of parameters that determine how inputs become outputs. Their strength is their ability to weigh all parts of an input at once instead of working through it one piece at a time.

What Can Generative AI Do?

Generative AI can create many types of content across several categories. Organizations are increasingly using generative AI in the workplace: As of late 2025, about 41% of workers reported using generative AI on the job, according to a Federal Reserve analysis on AI adoption in the United States. Here are some of the main kinds of content it can produce.

Text

Generative AI can generate coherent, contextually relevant text, ranging from instructions to documentation. Outputs of generative AI include brochures, emails, website copy, blogs, articles, reports, and creative writing. Generative AI tools can also handle repetitive writing tasks such as summarizing documents or drafting meta descriptions, freeing writers’ time for higher-value work. In a 2023 study published in Science, professionals who used ChatGPT for writing tasks reported that it improved their productivity, allowing them to finish their tasks 40% faster with an 18% increase in the writing’s quality.

Images and Video

Generative AI can create realistic images or original art and can perform style transfers, image-to-image translations, and image editing. Emerging video tools can generate animations from text prompts and apply special effects to existing footage faster and more cheaply than when using traditional methods. Adoption among creatives has been broad: In a 2024 Adobe survey, 83% of creative professionals said they used generative AI in their work.

Software Code

Generative AI can write original software code, autocomplete snippets, translate between programming languages, and summarize what the code does. It lets developers prototype, refactor, and debug quickly while offering a natural language interface for coding tasks. In a 2025 Stack Overflow developer survey, 84% of developers said they used or planned to use AI tools in their work.

Other Forms of Content

Generative AI works across many formats—from visuals and audio to synthetic datasets. Here are other forms of outputs that generative artificial intelligence can be used for.

Data Visualization and Communication

Generative AI helps turn complex information into clearer forms and can break dense problems into simpler, more learnable parts. This makes it a practical aid for explaining data and communicating findings to different audiences.

Design and Art

Generative AI can produce unique works of art and design or assist graphic designers in producing them. Applications include dynamic generation of environments, characters, avatars, and special effects for simulations and video games.

Sound, Speech, and Music

Generative models synthesize natural-sounding speech for voice-enabled chatbots, digital assistants, and audiobook narrations. The same technology generates original music that mimics the structure and sound of professional compositions.

Simulations and Synthetic Data

Generative AI creates synthetic data or structures from real or synthetic inputs. For instance, in drug research and development, it can produce molecular structures with specific properties, which helps in designing new pharmaceutical compounds.

Generative AI Ethics and Society

AI tools are essentially machines that process and learn from vast amounts of data, and then use what they’ve learned to help organizations and individuals improve their efficiency, accuracy, and innovation. While generative AI offers benefits, it also carries real risks. 

A primary concern about generative AI algorithm outputs is that they cannot be fully trusted to be accurate and an appropriate response to a query. In short, generative AI outputs can be wrong. Major chat tools such as ChatGPT and Claude offer disclaimers that AI can generate false content, often called hallucinations. Human oversight helps to catch these errors and biases.

Another key issue is bias in AI training data, which can be skewed by human, social, or organizational prejudices based on characteristics such as race, sex, age, or socioeconomic status. Bias can also affect how algorithms are designed, arising from choices made during data selection, sampling, processing, or checking.

The use of generative AI also raises critical questions about the future of the workforce and responsible AI development. Ensuring that the generative AI an organization adopts is designed and used ethically and that it is compliant with regulations is crucial in order to reduce the organization’s risk and allow it to build trust with its customers, its employees, and society at large. As the level of AI adoption grows, regulatory and risk frameworks are becoming more robust, and stronger AI governance is emerging.

Other risks include a lack of transparency about how algorithms reach their decisions and intentional misuse of generative AI, such as to spread disinformation and create deepfakes.

What Skills Are Needed to Use Generative AI?

Skills in three areas often emerge as important for those learning how to use generative AI: writing prompts, evaluating output, and using tools responsibly.

Learn to Write Effective Prompts

Writing good generative AI prompts takes skill. The quality of the output depends on the quality of the input. Generative AI relies on prompts and refines its outputs with more inputs. A clear first prompt and thoughtful follow-ups are more likely to lead to favorable results. Knowing how to frame requests, provide context, and iterate makes the difference between a generic answer and a useful one.

Evaluate and Verify AI Output

Checking the accuracy of a generative AI’s output is important. Generative AI models can produce content that contains errors, and their training data may have been biased. Human oversight helps catch these mistakes. The best approach is to treat every output as a draft that needs to be verified, not as a statement of fact. This approach protects organizations and individuals from sharing errors or biased content.

Use Generative AI Responsibly

Ethics, privacy, and integrity are crucial when using generative AI. The same tools that create value can also spread disinformation, generate deepfakes, or hide decision-making processes. Responsible use of generative AI requires understanding these risks, respecting others’ privacy, and following all governance and regulatory standards. 

Generative AI Resources

The following sources offer in-depth information for anyone interested in how to learn generative AI. They range from government reports and academic research to industry analyses that track how the technology is developing and being applied.

Prepare for a Career in Generative AI

As of 2024, LLMs surpassed human performance on traditional English-language benchmarks, according to Stanford’s index report on artificial intelligence. As the technology keeps advancing, preparing for a generative AI career starts with hands-on practice.

Individuals can begin by regularly using AI tools, so they can learn how they work, practice building prompts, and improve their fact-checking skills. They can also develop a solid grasp of the ethics and regulations for using generative AI. It can help to keep up with new advances in the technology as well, because the field keeps evolving. A few trends worth watching are: 

  • Agentic AI: From simple chatbots to agents that plan and carry out complex tasks on their own across different apps, agentic AI can perform duties as if it were a digital co-worker.
  • Robotics: AI is moving off the screen and into factories, warehouses, and shipping in the form of robots.
  • Hybrid computing: The use of quantum computing with AI is speeding up discoveries in fields such as medicine and materials science.

For those interested in how to learn generative AI, pursuing an advanced education in applied artificial intelligence can help you gain the skills you need to solve real-world problems with artificial intelligence. The world of work is changing quickly, and professionals with generative AI expertise are poised to be at the forefront of the new economy.

Last Updated: