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What Is Generative AI? A Comprehensive Guide

Updated on April 2, 2024AI

Artificial intelligence is everywhere. Excitement, fear, and speculation about its future dominate headlines, and many of us already use AI for personal and work activities.

Of course, it’s generative artificial intelligence that people are talking about when they refer to the latest AI tools. Innovations in generative AI make it possible for a machine to quickly create an essay, a song, or an original piece of art based on a simple human query.

So, what is generative AI? How does it work? And most importantly, how can it help you in your personal and professional endeavors?

This guide takes a deep dive into the world of generative AI. We cover different generative AI models, common and useful AI tools, use cases, and the advantages and limitations of current AI tools. Finally, we consider the future of generative AI, where the technology is headed, and the importance of responsible AI innovation.

Table of contents

What is generative AI?

Generative AI refers to a branch of machine learning in which models are trained on large amounts of raw data to create original outputs, such as text, images, or other content.

AI, or machine learning, makes statistically likely predictions based on historical data. For instance, traditional machine learning techniques might be used to predict the tone of a piece of writing from a list of possible tones, like “formal” or “funny.” These techniques build a model of the tone properties of text based on many examples and then use that model to evaluate new inputs.

Here’s an analogy: If someone were to show you a million examples of formal emails and then show you a new email, you might be able to determine if it has a formal tone or not. In the time it takes to process a thought, your brain would recognize certain characteristics in that email—the subject line, the length, and the polite vocabulary, perhaps—and realize that they fit your model of what “formal” looks like. At a very high level, traditional machine learning works in much the same way.

But what if you wanted to write a new formal email to your congressperson? This is the domain of generative AI. Like other forms of machine learning, generative AI is still trained on examples to construct a model of the world, allowing it to make predictions about new inputs. However, generative AI models typically need to be trained on much larger volumes of data to be successful, and the models themselves need to be more complex, too. Data scientists use machine learning technology, known as neural networks, to build generative AI models.

To write a formal email to a congressperson, a generative AI model would need to understand what “formal” writing looks like, how an email is typically structured, and what a “congressperson” is. Amazingly, no one hard-codes these concepts or explains how to put them together—the model learns this itself by being trained on mind-bogglingly large amounts of raw text (such as everything on the public internet). This allows it to essentially predict, word by word, a new email that is most likely to represent “a formal email to your congressperson.”

How generative AI works

To best understand how generative AI works, let’s break down its operations into simple steps.

1 A user enters a prompt

Generative AI responds to prompts entered by humans. For example, someone might enter a prompt such as “Write a professional letter of acceptance for an offer of employment as a project manager.” The more specific and well-written the prompt, the more likely the model is to produce a satisfactory output. You might hear the term prompt engineering, which refers to the process of tweaking a prompt’s phrasing or including additional instructions to get higher-quality, more accurate results from a generative AI tool.

Prompts aren’t always provided as text. Depending on the type of generative AI system (more on those later in this guide), a prompt may be provided as an image, a video, or some other type of media.

2 The generative AI tool analyzes the prompt

Next, generative AI analyzes the prompt, turning it from a human-readable format into a machine-readable one. Sticking with text for the purposes of this example, the model would use natural language processing (NLP) to encode the instructions in the prompt.

This starts with splitting longer chunks of text into smaller units called tokens, which represent words or parts of words. The model analyzes those tokens in the context of grammar, sentence structure, and many other kinds of complex patterns and associations that it’s learned from its training data. This might even include prompts you’ve given the model before, since many generative AI tools can retain context over a longer conversation.

3 The tool generates a predictive output

Using everything that the model has encoded about the prompt, it tries to generate the most reasonable, statistically likely response. In essence, the model asks itself, “Based on everything I know about the world so far and given this new input, what comes next?”

For example, imagine you’re reading a story, and when you get to the end of the page, it says, “My mother answered the,” with the next word being on the following page. When you turn the page, what do you think the next word is going to be? Based on what you know about the world in general, you might have a few guesses. It could be phone, but it could also be text, call, door, or question. Knowing about what came before this in the story might help you make a more informed guess, too.

In essence, this is what a generative AI tool like ChatGPT is doing with your prompt, which is why more specific, detailed prompts help it make better outputs. It has the start of a scenario, like “Write a funny poem about a dog.” Then it tries to complete the story word by word, using its complex model of the world and the relationships in it. Crucially, generative AI tools also go through what’s called reinforcement learning with human feedback to learn to prefer responses that humans will approve of.

If you’ve played around with generative AI tools, you’ll notice that you get a different output every time—even if you ask the same question twice, the tool will respond in a slightly different way. At a very high level, the reason for this is that some amount of randomness is key to making the responses from generative AI realistic. If a tool always picks the most likely prediction at every turn, it will often end up with an output that doesn’t make sense.

Generative AI models

There are many techniques used for generative AI. At their core, they all use neural networks, which is a type of architecture that resembles how the human brain works. You may also hear the term deep learning, which simply refers to a neural network with more than three layers (which is all neural networks beyond the most basic ones).

A neural network consists of layers of interconnected nodes. Each node has its own statistical model that’s specialized at processing a single facet of the input based on what it’s learned from the training data. Similar to how parts of the brain are dedicated to different tasks—certain neurons might fire when you see a familiar face—different parts of the neural network recognize different patterns and relationships.

While each individual decision is simple (say, one node might specialize in recognizing whether a word is a noun or not), the final prediction is a domino effect of all these decisions coming together to result in a very complex output.

There are a variety of ways that data scientists actually use neural networks to build generative AI models. Some of the most common kinds of architectures are:

Generative adversarial networks (GANs) and variational autoencoders (VAEs): These two classes of models, used for image generation, started making a stir in the mid-2010s, foreshadowing the current generative AI buzz. With GANs, one neural network generates an output, and the other network tries to determine if it’s real or fake—this back-and-forth makes the model smarter as it trains. VAEs generate encodings of the parts of an image and then sample parts of the encodings and decode them to create entirely new images.

Diffusion models: In a diffusion model for generative AI, training data is destroyed via the addition of Gaussian noise and then recovered by reversing the addition of that noise. You can think of adding Gaussian noise as scrambling the pixels in the image. The noise is “Gaussian” because it’s added based on probabilities that lie along a bell curve. When the model reverses the noise, it makes predictions by decoding those probabilities, which results in an original image each time, even with the same prompt.

Large language models (LLMs): This type of model uses a neural network architecture called a transformer. Transformers can essentially consider all of the words in an input sequence at the same time and figure out how they’re related, making them an especially useful technique for understanding and generating text. While the concept of transformers has been around since 2017, applying vast amounts of training data and improving model performance with human reinforcement has led to the breakthroughs we see today with generative AI natural language tools.

Hybrid models: A hybrid model combines rule-based computation with machine learning and neural networks to bring human oversight to the operations of an AI system. Basically, you could take any of the above generative AI models and subject them to a rules- or logic-based system after or during their operations.

Generative AI tools

You may have already used some of the more prominent generative AI tools for work, research, or personal activities. OpenAI’s ChatGPT, for example, is commonly used for everything from writing party invitations to finding answers to esoteric and specialized questions.

ChatGPT uses a LLMs to process users’ natural language prompts and deliver straightforward, conversational responses. The tool resembles a chatbot or a message exchange with an actual person—hence its name. Google’s Gemini is another generative AI tool that uses an LLM to provide unique responses to user prompts. It works much like ChatGPT.

LLMs aren’t the only type of generative AI available to consumers. DALL-E, another generative AI innovation from OpenAI, uses a diffusion model to generate original images. For example, a user might prompt DALL-E to create an image of a frog riding a horse on a basketball court in the fauvist style of Henri Matisse. Relying on its neural network and a vast dataset, the tool would create an original image incorporating the user’s desired stylistic elements and specific requests for image content.

Those are some of the more widely known examples of generative AI tools, but various others are available. For instance, Grammarly is an AI writing tool that uses generative AI to help people improve the clarity and correctness of their writing wherever they already write.

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With Grammarly’s generative AI, you can easily and quickly generate effective, high-quality content for emails, articles, reports, and other projects. Examples include group emails to your department inviting them to a company function or executive summaries for business documents.

Generative AI use cases and applications

The potential uses for generative AI span multiple industries and applications, whether professional or personal. Here are a few generative AI use cases to consider.


  • Generating patient prescriptions based on diagnostic criteria and clinician notes
  • Producing summaries based on notes taken during an appointment
  • ER or telehealth triage tasks—generative AI tools can note a patient’s symptoms and produce a summary for clinicians to view before meeting with the patient
  • Spotting instances of insurance fraud within large volumes of patient financial data

Banking and Finance

  • Autodetection of potential fraudulent activity
  • Generating financial forecasts
  • Providing specialized and nuanced customer support
  • Creating marketing plans based on financial data about the past performance of different products and services


  • Generating different versions of landing pages for A/B testing of headlines and marketing copy
  • Creating unique versions of otherwise identical sales pages for different locations
  • Getting new content ideas based on performance data for existing content
  • Quickly creating new images or infographics for marketing campaigns
  • Generating unique musical scores for use in marketing videos

Entertainment and performances

  • Creating unique imagery for promotional materials
  • Building new, immersive landscapes and scenarios for virtual reality
  • Rapid storyboarding for new scripts or ideas in film, television, or theater
  • Improving computer-generated imagery by portraying characters in difficult- or impossible-to-film scenarios

Advantages and benefits of generative AI

As illustrated by the above use cases, generative AI brings extraordinary advantages in terms of efficiency and automation. Simply put, it allows us to do more work faster by automating the more tedious aspects of our jobs.

This benefit is perhaps most apparent in the healthcare examples, which include triage-related tasks and clinician administration. By outsourcing those activities to generative AI, clinicians can see more patients, spend more time with each one, and provide better care.

For marketers, the amount of time saved by generative AI is also significant. Creating multiple versions of marketing campaigns, tailoring messages to different demographics, and commissioning unique visual creative assets account for a lot of marketers’ time. Offloading such tasks to generative AI gives marketers additional professional and creative bandwidth. In short, it allows them to focus more attention on activities that require their expertise.

Generative AI allows people to outsource time-consuming tasks with a moderate-to-heavy cognitive burden to automated tools. The result is additional time for those individuals to focus on high-value activities. Another benefit of generative AI: It can be a creative partner. It can help people brainstorm new ideas and strategies or create many rough drafts of a piece of writing or art that can then serve as inspiration or provide a sense of direction.

Disadvantages and limitations of generative AI

Generative AI is an exciting technology, but that doesn’t mean it’s perfect.

You may have heard about the attorneys who, using ChatGPT for legal research, cited fictitious cases in a brief filed on behalf of their clients. Besides having to pay a hefty fine, this misstep likely damaged those attorneys’ careers. Generative AI is not without its faults, and it’s essential to be aware of what those faults are.


Sometimes, generative AI gets it wrong. When this happens, we call it a hallucination.

While the latest generation of generative AI tools usually provides accurate information in response to prompts, it’s essential to check its accuracy, especially when the stakes are high and mistakes have serious consequences. Because generative AI tools are trained on historical data, they might also not know about very recent current events or be able to tell you today’s weather.


Several prominent generative AI tools output information that contains racial and/or gender bias. In some cases, the tools themselves admit to their prejudice.

This happens because the tools’ training data was created by humans: Existing biases among the general population are present in the data generative AI learns from.

Privacy and security concerns

From the outset, generative AI tools have raised privacy and security concerns. For one thing, prompts that are sent to models may contain sensitive personal data or confidential information about a company’s operations. How will these tools protect that data and ensure that users have control over their information?

As with any software, there’s also the potential for generative AI tools to be hacked. This could result in inaccurate content that damages a company’s reputation or exposes users to harm. And when you consider that generative AI tools are now being used to take independent actions like automating tasks, it’s clear that securing these systems is a must.

When using generative AI tools, make sure you understand where your data is going and do your best to partner with tools that commit to safe and responsible AI innovation.

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The future of generative AI

For organizations, generative AI isn’t just software. It’s a junior member of your team. This is true in virtually every industry, so it’s essential to be ready for how AI will change the way you work.

According to Gartner, businesses should create AI plans that account for:

  • Opportunity ambition: The specific advantages you or your organization hopes to enjoy after deploying AI tools.
  • Deployment: Decide whether to use off-the-shelf tools, build in-house, or train the model using your own proprietary data.
  • Risk: Organizations need to account for all potential risks inherent in AI, such as reliability, security, data privacy.

According to IBM, we should also expect governments around the world to make AI management a priority. The European Union, for example, is currently working to categorize AI into different risk categories and mandate certain rules about its use and deployment.

With those activities in mind, it’s essential to consider the ethical implications of generative AI and what it means to pursue responsible development. The World Economic Forum has published a list of factors that AI innovators must consider when developing and deploying new AI systems. They include:

  • Efficacy of the AI system for what it is intended to do
  • Resilience against security threats
  • Bias mitigation to avoid the unjust treatment of marginalized groups
  • Explainability, which means the outputs of an AI system should be understandable to users
  • Privacy protection, which includes data minimization principles

The bottom line is that AI is here to stay. In the months and years ahead, expect institutions and governments to pay ever-increasing attention to the challenge of responsible innovation in the world of generative AI.

Conclusion: Make the most of generative AI

Generative AI is a force to be reckoned with across many industries, not to mention everyday personal activities. As individuals and businesses continue to adopt generative AI into their workflows, they will find new ways to offload burdensome tasks and collaborate creatively with this technology.

At the same time, it’s important to be aware of the technical limitations and ethical concerns inherent to generative AI. Responsible development is one thing—and it matters—but responsible use is also critical. Always double-check that the content created by generative AI tools is what you really want. And if you’re not getting what you expected, spend the time understanding how to optimize your prompts to get the most out of the tool.

By staying abreast of the latest innovations in generative artificial intelligence, you can improve how you work and enhance your personal projects. While exciting, the current generation of AI tools offers merely a glimpse of what lies beyond the horizon.

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