What Is Generative AI: Ultimate Guide

Generative AI is a type of artificial intelligence that uses neural networks, deep learning models, complex algorithms, and large and varied training datasets to produce original content based on user input.
The content that generative AI creates includes written text, images, video, audio, music, synthetic data, and computer code, with new use cases and products emerging on a near-daily basis.
Generative AI has recently garnered widespread attention because major breakthroughs in the technology are accelerating and now reaching the general public.
For example, OpenAI’s ChatGPT — which is publicly available in both free and paid versions — can generate grammatically correct and logical text responses that appear to be written by humans, and OpenAI’s DALL-E tool can produce photorealistic images based on user descriptions. Other companies, including Google, Microsoft, and Meta, have also developed sophisticated generative AI tools that can produce authentic-looking text, images, or computer code with minimal human intervention or technical know-how.
In this guide, we’ll talk through more of what generative AI actually is and how it works, as well as some practical tips for understanding how it can be used and how your business can benefit from incorporating this technology into your workflows and routines.

TABLE OF CONTENTS
ToggleHow Does Generative AI Work?Historical Evolution & Significance of Generative AIGenerative AI vs. AI5 Key Generative AI Techniques & Models3 Real-World Generative AI Use-Case Examples5 Benefits of Generative AISome Limitations of Generative AITop 8 Best Practices for Generative AIRisks & Ethical Considerations for Generative AITop 3 Generative AI Tech ProvidersThe Future of Generative AIHow to Get Started Implementing Generative AIGenerative AI: Frequently Asked Questions (FAQs)Bottom Line: Generative AI’s Rapid Evolution 

How Does Generative AI Work?
Generative AI models work through the training they receive from massive training datasets and neural networks, which teach the models how to reason in ways akin to a human brain. For some of these models, researchers feed enormous volumes of data — words, pictures, music, or other content — into a deep learning system called a Generative Adversarial Network (GAN) framework. The supervised neural network sifts through the data and uses a system that rewards successes and penalizes errors, mistakes, and failures.
Over time and with frequent fine-tuning and improvements, a generative AI model can learn how to identify and understand complex relationships. Beyond GAN, other common generative AI model training techniques include transformer-based model training and diffusion model training.
Once a model has received its initial training and is released, users may access it in different ways and for different reasons. If it’s an AI chatbot like Gemini or ChatGPT, for example, users may choose to access the tool from their desktop or a mobile app, requiring little to no additional investment or hardware to get started. All they need to do is input their queries, being sure to write clear, natural-language inputs; these tools will generate responses accordingly.
If the customer is more interested in using a foundation model so they can develop or fine-tune their own models and project use cases, they’ll likely need to work with the original vendor’s API keys and documentation. They may also need to invest in GPUs, CPUs, and other high-powered computer hardware and software in order to operate these tools in their own environments.
For more information about generative AI providers, read our in-depth guide: Generative AI Companies: Top 12 Leaders
Historical Evolution & Significance of Generative AI
Artificial intelligence research began to take shape during the 1950s when Alan Turing and other scientists began to explore ways to create computing frameworks that could duplicate human thinking.
By the 1960s, so-called Markov models began to appear. These probability-based algorithms could generate speech or text based on basic mathematical models, though with limited success. By the 1990s, more sophisticated AI technologies and foundational tools were appearing, including early AI chatbots and the first recurrent neural network architecture.
In the 2010s and 2020s, the development of transformer-based architecture, GPUs, and advances in deep learning have ushered in far more advanced AI. Today, recurrent neural networks can generate content in a way that approximates — and in some cases exceeds — the output of human artists, musicians, and writers.
At this point, artificial intelligence and generative AI are fundamentally reshaping the way people and businesses act, interact, and process information. Whether they’re simply experimenting with free generative AI chatbots or they’re fine-tuning and embedding complex generative AI tools in their enterprise environments, a significant number of business professionals and casual users alike have begun to use generative AI tools in their daily lives. As these tools continue to mature, their benefits expand into areas like pharmaceuticals and drug discovery, healthcare and diagnostics, and generative AI cybersecurity protections.
To learn how generative AI is used in robotics, read our guide: Generative AI Is Coming to Robots, Courtesy of NVIDIA
Generative AI vs. AI
In essence, generative AI is a narrow type and application of the broader artificial intelligence umbrella of technologies.
Generative AI technology is specifically designed and trained to generate new content. In contrast, AI as a whole encompasses a wide range of use cases and applications, including robotic process automation, AI-supplemented cybersecurity, sentiment analysis, predictive maintenance, image recognition, and chatbots with either predetermined or generative responses.
Both of these AI technology types can effectively support human users with a variety of tasks and goals, yet at this time, both types of technology are not mature, consistent, or autonomous enough to stand in for humans.
For a fuller understanding of the different types of AI, see our guide: Generative AI vs. AI
5 Key Generative AI Techniques & Models
The best generative AI techniques and models all depend on what you’re trying to accomplish and if you’re planning to train your own models from scratch or fine-tune an out-of-the-box solution. Learn more about some of the model architectures and related training techniques:

Transformer-based model training: Through a combination of attention mechanisms — which allow models to analyze data all at once rather than sequentially — and encoders and decoders, generative models are trained to create logical, original content in the style of training data. This type of model training is most commonly used for text and code generation models. Model example: Gemini (formerly Bard).
Variational autoencoder (VAE) training: Input data is sent to a latent space (latent variable generative model training) where the model can more easily learn how to accurately depict images and audio. Model example: Certain iterations of DALL-E.
Autoregressive model training: Trained to sequentially predict the most logical next segment of data. This type of model training is most commonly used for coding and developer use cases. Model example: GitHub Copilot.
GAN model training: Two different generative AI models compete to generate the most accurate data and outputs; their ongoing competition trains them both to produce better data over time. Model example: Artbreeder.
Diffusion model training: A type of latent variable generative model, diffusion model training involves diffusing original data with noise and slowly removing it to create new data that closely resembles the old data. Model example: Stable Diffusion XL.

3 Real-World Generative AI Use-Case Examples
Generative AI can be used for much more than simple text generation and Q&A. In business contexts, users are beginning to take advantage of generative AI capabilities for these use generative AI cases and many more:
Advanced Data Analytics & Recommendations
Rather than simply providing predictive and prescriptive analytics results, generative AI data analytics solutions can pull data from more locations and provide smart explanations and recommendations for how to improve these numbers in the future. Marketing, sales, e-commerce, and product development teams are using this type of tool to help them brainstorm new solutions for better customer engagement.
Video & Image Generation
AI image and video generators are popping up all over the place and are being used for everything from just-for-fun creative projects to social media posts to video game graphics. With intelligent editing tools, natural-language-driven content creation, AI avatars, and voice synthesis, these modern media creation tools make art and video projects more accessible to more users.
Compare two of the leading image generation solutions: Midjourney vs. DALL-E: Best AI Image Generator
Coding & App Development
Several generative AI platforms have been developed with the needs of advanced and novice developers in mind. These tools allow users to create new code through natural language requests. Similarly, they can troubleshoot existing code, automate code completions, and receive recommendations for how to further optimize their coding projects, all through chat and natural language interfaces.
5 Benefits of Generative AI
When it’s used thoughtfully and with clear goals in mind, generative AI offers users a range of unique benefits, including the following:
Operational Efficiencies
Generative AI assistants and copilots are great at assisting users with routine tasks that can take up a lot of time. Some of the work that AI can now help with includes taking and summarizing meeting notes, updating content in documents and spreadsheets, and sifting through emails to determine which ones are most urgent. With AI handling some of these types of tasks, employees have more time to focus on more strategic tasks for the business.
With Copilot for Microsoft 365 in Teams, the Copilot tool can offer quick meeting summaries and action items based on past or ongoing meetings. Source: Microsoft.
Accessible Tools for Brainstorming
If you’re feeling stuck on a project or are a solopreneur who needs someone to bounce ideas off of, several generative AI tools are up to the task. Inflection’s Pi, for example, is a capable conversational AI agent that does a good job of handling contextualized, back-and-forth conversations. While it won’t be the best solution for artists who want to talk about or work through their projects, text-based queries work well here.
Scalable Content Generation
When generative AI chatbots and models are given clear instructions for content generation, the initial drafts they produce are often close to human quality and take a fraction of the time.
For example, ChatGPT can draft a 2,000-word article on a complex topic — even including relevant headers — in less than a minute.
These tools can be used to generate different kinds and quantities of content as well. For instance, if you are experiencing a creative block as a social media manager, with just a few pieces of information fed into a generative AI tool, you can generate dozens of social media caption options to help you move forward.
This simple prompt enabled ChatGPT to generate 50 relevant social media captions that would have taken me significantly longer to write. Source: Shelby Hiter via ChatGPT.
In-depth Data Analytics & Recommendations
Generative AI data analytics tools go beyond other data tools’ features, offering intelligent recommendations for how to improve data in future iterations. These analyses can happen in real time, so users can quickly pivot their marketing or sales strategies if AI detects issues with their current strategies.
Coding & Product Development Support
Whether you’re a new developer or an experienced coder looking to work through complex problems, generative AI tools are quickly becoming accurate coders, especially for code completion and quality assurance tasks. This can also be incredibly helpful for product and app development when scalable, repeatable code production on a timeline can be difficult for human task forces.
For a fuller understanding of generative AI, read our guide: The Benefits of Generative AI
Some Limitations of Generative AI
While generative AI can complete many tasks similarly to how a human would do them, these tools still have limitations that can impact your results and possible projects:

AI hallucinations: Because of how generative AI models are trained, they may occasionally give a completely false or bizarre answer to user queries with the utmost confidence – these are known as AI hallucinations. It’s important to fact-check AI content, especially if it sounds strange.
No independent thinking capabilities: Generative AI tools are not autonomous thinkers, though their responses sometimes sound like they’re coming from a human. They are incapable of original thoughts — all content they generate is based on the training data and algorithms running in the background.
Little historic data storage and retrieval: While some generative AI tools store conversational history for a limited time, many do not store historic data in a way that users can easily access. As such, it’s difficult for users to continue or recall past conversations with AIs as they might do with a friend.
Limited cybersecurity and data security protections: Some generative AI tools have basic security and compliance features built in, but most will not have the enterprise-level data security protections that users require. These users will need to invest in third-party, comprehensive cybersecurity solutions for the best possible results.
Biased training data: Generative AI tools are only as good as the datasets and algorithms that train them. If data is sourced in an incomplete, inaccurate, or unethical manner, the resulting content generated by the tool may reflect those biases and errors.
Rare citations and sourcing information: Generative AI isn’t the most reliable way to go about serious research, especially since most of these tools do not mention any specific citations or references when stating a fact.
Lack of real-time data: Though this is changing quickly with tools like Google’s Gemini, most generative AI tools are not connected to the internet or other real-time data sources. This means that the answers they provide may not take into account recent, relevant events.

Top 8 Best Practices for Generative AI
The following generative AI best practices can benefit both business leaders and individual users of this type of technology:

Set policies: Set an AI policy that details AI governance, AI ethics, and usage rules for your organization.
Ensure standards: Protect and determine standards for your data proactively.
Staff training: Train employees and any other users on generative AI tools and how and when to use them.
Stay aware of regulations: Pay attention to industry-specific, regional, national, and international regulations and compliance laws, especially as these rules evolve around AI technology.
Monitor content: Quality-check generated content against reliable sources to ensure accuracy.
Select partners carefully: Partner with ethical AI technology providers that transparently discuss how they train their models and source their data.
Evaluate models: If you choose to develop your own models, establish a process for periodically evaluating model performance; ideally, add a peer review component so all users can share feedback if something is amiss.
Specific prompts: Be specific when prompting for the best possible outcomes.

Risks & Ethical Considerations for Generative AI
Not surprisingly, the rise of Generative AI has unleashed concerns, especially in the ways that it can effectively mimic the work and conversations of humans. Learn more about some of the possible risks of generative AI and ethical concerns that come with the rise of generative AI:
Inaccuracies & Hallucinations
For reasons mostly unknown at this time, the complex training that generative AI tools receive can occasionally cause them to hallucinate, or produce wildly inaccurate (and sometimes offensive) content.
AI-generated content requires human review because it isn’t always complete or accurate, even if it sounds fairly logical. Blindly plugging in text generated from an AI tool into your projects could lead to a variety of problems, ranging from accusations of bias to legal issues related to plagiarism.
Deepfakes & Misinformation
Generative AI tools for images, videos, and audio synthesis are being used to create more believable deepfakes, or digital media that convincingly mimics an actual person to their detriment. With the maturity and believability of this technology, deepfakes and similar generative AI products have been used to falsely incriminate world leaders, create explicit and offensive imagery of celebrities, and more.
Legal Issues & Plagiarism 
Businesses must be cautious about the types of music, images, and other materials they use when derived from generative AI. Because these models are often trained on data or actual content produced by writers, musicians, and painters, this usage can raise questions about ownership, control, and copyright.
For this reason, generating a photorealistic image that’s similar to the specific style of an artist could raise questions or even lead to a lawsuit or public backlash. There are also growing concerns about AI-enabled plagiarism in academic settings.
Privacy & Security
AI privacy Issues and AI cybersecurity concerns are at the forefront of generative AI. Some data that’s used to train generative AI models may inadvertently contain private data or information that could be exposed at a later date. This danger may come in the form of a model’s initial training data or in the data it collects from user queries and submissions.
Equally concerning: cybergangs and other criminals have already begun to use generative AI to produce highly convincing documents, software, and images that become part of social engineering campaigns.
Societal & Environmental Impact 
The overall impact of generative AI on the workforce and society at large is prompting serious discussion. Some observers, such as New York Times technology columnist Kevin Roose, have raised concerns about the technology being used to manipulate humans in harmful and destructive ways. In addition, critics have voiced concerns about the technology carrying out its own dangerous acts if it achieves higher levels of autonomy.
Currently, there are many concerns about the processing power and requirements to produce this type of technology as well; some AI companies are coming forward with greener production methods, but at this time, there’s little regulation for this industry that is already harming the environment at scale.
To learn more about the many ethical issues around generative AI, see our guide: Generative AI Ethics: Concerns and Possible Solutions
Top 3 Generative AI Tech Providers
While many generative AI leaders, including AWS and IBM, have come to the fore in recent months and years, these three are the clear front runners today:

OpenAI: A startup that has blossomed into a multi-billion-dollar empire, OpenAI is the force behind GPT-4, ChatGPT, DALL-E 3, and several other content generation tools. Its focus areas include text, image, and code generation.
Microsoft: Long a leader in the business and enterprise technology space, Microsoft has quickly made a name for itself in generative AI, especially through its strategic and close partnership with OpenAI. Its Copilot technologies, including Copilot for Microsoft 365, provide intelligence AI assistance to users across a variety of workplace tasks.
Google: Google (Alphabet) was one of the earliest generative AI technology pioneers with its 2017 paper that established the basis for modern transformer-based architecture. Today, it offers users access to a tool called Gemini, a direct ChatGPT competitor that can supplement its responses with real-time data and images from the internet.

Beyond these larger enterprises, many other companies and early startups are creating interesting generative AI solutions.
Learn about them here: 50 Generative AI Startups to Watch This Year
The Future of Generative AI
While nobody can predict the exact trajectory of generative AI, it’s already clear it will profoundly impact businesses and society at large. As the technology advances, its capabilities and relevant industry use cases continue to expand. Nowhere is this more evident than in the pharmaceutical drug discovery and medical diagnostics companies that are releasing new solutions and use cases regularly.
Years from now, it’s possible that generative AI will produce better final drafts than professional writers and generate better art and design projects than professional human artists and graphic designers. In many ways, we may see generative AI take over jobs in both creative and industrial fields that were previously handled by human workers. However, we’ll likely see the creation of new jobs as well, especially for work like AI quality assurance, training, and testing.
To understand the fuller ecosystem of generative AI, see our guide: Generative AI Landscape: Current and Future Trends 
How to Get Started Implementing Generative AI

Establish an internal AI taskforce: This team can consist of C-suite members, technical team members, and other organizational leaders and stakeholders. Regardless of its demographics, this group will lead initiatives surrounding AI investments, buy-in, and best practices for the organization.
Determine your goals and hopeful outcomes for AI usage: Before investing in expensive tools and possible hardware and software to get started, first decide what kind of generative AI your organization wants and when, how, and why it will be used. This will help you narrow your search to the most useful tooling options.
Research available tools and AI vendors: Your search should focus on the reputation of vendors and tools, the relevance of tools to your organizational goals, and how well prospective tools align with your compliance and ethical requirements.
Select AI model(s) that work for your organization: Now it’s time to select a model that will align with your goals as an organization. Beyond focusing on models from reputable AI vendors, you’ll also want to consider your unique requirements, like budget and the level of technical skill within your workforce to use these kinds of tools.
Determine how to integrate AI with your current tool stack: Though you can still get a lot out of standalone generative AI solutions, businesses will be able to do more with their data and projects if they select a model that integrates directly with their most-used applications, documents, and other company resources. Many AI vendors offer accessible API keys, plugins, and other integration options to help you unify your tech stack with AI.
Develop an AI usage and governance policy for your organization: To make sure everyone is on the same page about how AI should be used within the organization, create a comprehensive policy and distribute it throughout your workforce. This policy should detail any roles or responsibilities, how to use data in a way that complies with relevant regulations, and more.
Train and provide resources to all users for appropriate AI usage: Simply handing a policy to your employees is not enough to enforce proper AI use. That’s why it’s a good idea to train relevant stakeholders on how they can use AI to make their workflows more efficient and effective.

To find out what today’s executives must be aware of, read our guide: Generative AI: 5 Things Business Leaders Must Know
Generative AI: Frequently Asked Questions (FAQs)
What are some examples of generative AI tools?
Generative AI tools include generative AI chatbots, such as ChatGPT, Gemini, and Claude, that can provide logical and contextualized responses to complex user queries. Other examples include AI assistants and copilots, such as Copilot for Microsoft 365 and Notion AI, which can help users quickly create content, summarize notes, and complete other clerical, back-office, and operational tasks more efficiently. Generative AI tools may also cover tasks like image, video, or audio generation; synthetic data generation; or code generation and quality assurance.
To see a list of today’s top generative AI tools, see our guide: Top Generative AI Applications and Tools
What are DALL-E, ChatGPT, and Gemini?
DALL-E, ChatGPT, and Gemini are generative AI tools from leading AI companies. DALL-E and ChatGPT are both solutions offered by OpenAI; DALL-E can be used for image generation, while ChatGPT is designed for a variety of content generation types and chatbot tasks, including text, code, and imagery. Gemini — previously known as Bard — is Google’s answer to ChatGPT that provides users with text and image generation with data powered by real-time internet search results.
What are the concerns surrounding generative AI?
Concerns surrounding generative AI include environmental impact, ethical issues with data sourcing and plagiarism, data security and misuse issues, deepfakes and otherwise harmful content, the replacement of human workers with AI tools, and the fear that AI will eventually become autonomous. Some of these concerns can be mitigated with a thoughtful approach to AI policies and governance.
Bottom Line: Generative AI’s Rapid Evolution 
If Generative AI can match or exceed human performance for many tasks, the nature of work and many individual roles within organizations will change dramatically. Some roles and job functions will disappear, while new roles will likely replace them or be designed to collaboratively partner with AI tools.
In the end, one thing is certain: Generative AI is here to stay. As neural networks and GPUs continue to advance and AI algorithms become more refined, the ability of AIs to perform human tasks is increasing exponentially. Whether Generative AI will lead to singularity — the hypothetical point in which AI exceeds human intelligence — remains to be seen. However, it’s clear that generative AI is poised to change the way we work, live, and interact with each other in a multitude of ways, as it’s already doing.
Find out about the leading generative AI tools that can boost your productivity: 20 Top-Rated Generative AI Tools & Applications in 2024
The post What Is Generative AI: Ultimate Guide appeared first on eWEEK.