What is Generative AI

Generative artificial intelligence (genAI) is a branch of artificial intelligence that creates new, original content—text, images, code, audio, and video—by learning and replicating patterns from existing data. It’s the latest evolution of machine learning technology and has become part of everyday life. Whether it’s the “you may also like” suggestions when you’re shopping online, automated edits in your photo apps, or a chatbot answering your support questions, generative AI is changing the way you create, communicate, and get work done.

Key takeaways

• Generative AI is a branch of artificial intelligence that creates new, original content—text, images, code, audio, and video—by learning patterns from existing data.
• Unlike traditional AI, which recognizes patterns and makes predictions, generative AI's primary purpose is to produce something new.
• Generative AI is built on neural networks, using architectures such as generative adversarial networks (GANs), variational encoders (VAEs), transformers, and diffusion models.
• Industries from healthcare and finance to creative work and software development use generative AI for content creation and automation.
• Generative AI carries real risks, including hallucinated or biased outputs, copyright and privacy disputes, deepfakes and misinformation, and disruption to certain jobs.

Table of contents

• How is generative AI different from traditional AI?
• How did generative AI models evolve over time?
• How do different types of generative AI work?
• What is the technology behind generative AI?
• How are generative AI foundation models trained?
• How is generative AI used across different industries? 
• What are the challenges in implementing generative AI?
• How do I add generative AI to my applications? 
• Conclusion: Embracing generative AI
• FAQs
• Related resources

How is generative AI different from traditional AI?

The core difference between generative AI and traditional AI rest on what each one is built to do:

• Traditional AI analyzes: It recognizes patterns, makes predictions, classifies data, and executes rules-based tasks—drawing conclusions from information that already exists.
• Generative AI creates: Its primary function is to produce new, original content that didn't exist before.

Generative AI starts with a prompt. The prompt can come from a human or a machine, like an AI agent. Prompts can contain text, images, videos, or any input the AI system can process. To answer the prompt, the model accesses a wide range of data, such as books, websites, code repositories, and the internet, and uses reasoning, patterns, and statistical relationships learned during training to produce new content tailored to the specific request.

Predictive text was just the start. Early applications of generative AI featured basic functions, such as predictive text and voice assistants, but the large language models (LLMs) that power today's generative AI models are more sophisticated. They can write full-length articles with sources, design custom visuals, compose music, or generate working code. Beyond these applications, other generative AI models can be trained on specialized data to support industries like healthcare, finance, law, and even software development.

How did generative AI models evolve over time?

The origins of the generative AI we use today go back nearly 70 years. To understand how we got here—and where this technology is headed—let’s take a look at the breakthroughs that made this moment possible.

Machine learning foundations: 1950s through 1980s

The building blocks—first learning from data, then learning from mistakes—were laid decades before generative AI arrived.

Early machines could learn, but only simple things. In 1957, Frank Rosenblatt built the perceptron, the first AI system to learn patterns from data rather than having humans pre-program the decision-making logic. But these single-layer perceptrons had significant limitations: they could only solve relatively simple problems, failing when patterns became more complex or nuanced.

Next, machines began to learn from their mistakes. Another breakthrough came in 1970 when Seppo Linnainmaa introduced the reverse mode of automatic differentiation (the core mathematical technique later widely known as backpropagation), which provided an efficient way to compute gradients. In other words, this technique allowed neural networks to learn from their mistakes by calculating how much each connection contributed to errors and then adjusting accordingly.

A 1986 paper finally cracked the harder problems. The seminal 1986 paper by David Rumelhart, Geoffrey Hinton, and Ronald Williams (often cited as their Nature paper, Learning Representations by Back-propagating Errors) demonstrated how this backpropagation technique could successfully train multilayer neural networks, overcoming the limitations of single-layer perceptrons.

Memory: 1990s

In the 1990s, researchers introduced recurrent neural networks (RNNs), giving models a kind of short-term memory that made them useful for tasks like language and speech. But early RNNs struggled with longer or more complex input. That changed in 1997 with the introduction of long short-term memory (LSTM) networks, which allowed models to capture more complex patterns over time. These advances in neural network design, paired with improved hardware, laid the groundwork for training the deep generative models that support today’s systems.

Competition, conversation, and creation: 2014-2022

Three breakthroughs in this period turned the early machine learning models into the generative AI we recognize today: generative adversarial networks (GANs), transformers, and diffusion models.

Generative adversarial networks

Generative adversarial networks, introduced by Ian Goodfellow and his colleagues in 2014, involve two neural networks competing against one another to generate content that is indistinguishable from human-created content.

One network—the generator—creates synthetic content, while the other—the discriminator—tries to detect whether it's real or fake. This adversarial setup pushes the generator to improve with each round, eventually producing realistic outputs like high-resolution images and videos. GANs are widely used to generate high-resolution photos or video frames, enhancing image quality and filling in missing visual data.

Beyond that, they play a role in several fields:

• Art generation: Creating original images, artwork, and visual designs
• Photo restoration and colorization: Repairing damaged photos and adding color to black-and-white images
• Medical imaging: Enhancing scans and generating synthetic medical images
• Drug discovery: Proposing new drug compounds for researchers to test
• Data augmentation: Expanding training datasets for other models

Transformer architecture, 2017

Transformers, introduced in 2017, are a type of neural network architecture that make it possible for researchers to train larger models without having to label all the data in advance. They analyze entire sequences of data simultaneously instead of processing one word at a time. This improvement enabled the rise of modern AI chatbots and language models and paved the way for translation, summarization, and dialogue systems.

Used widely today: The generative pre-trained transformer architecture is foundational to many generative AI models we see today, such as OpenAI’s ChatGPT, Google’s Gemini, and Anthropic’s Claude.

Diffusion models, early 2020s

Diffusion models are the engine behind today’s leading image generators, such as DALL-E and Midjourney. They reverse the process of adding noise to an image, starting with static noise and gradually revealing a clear, high-quality image through multiple steps of refinement. These techniques help generate realistic images that are nearly indistinguishable from real photographs. Stable Diffusion is one popular implementation of this approach.

Mainstream adoption, multimodality, and agents: 2022 to today

The pace of generative AI didn't slow after the early 2020s. The November 2022 release of ChatGPT brought generative AI into mainstream use almost overnight, turning a research trajectory into an everyday tool for millions of people. Since then, the technology has moved in several directions at once:

• Multimodal models handle text, images, audio, and video within a single system rather than specializing in one.
• Reasoning models work through a problem in steps before producing an answer, improving accuracy on complex tasks.
• AI agents don't just respond to a prompt but take actions, use tools, and complete multi-step tasks on a user's behalf, often prompting the underlying model themselves rather than waiting for a person to request it.

How do different types of generative AI work?

Even though many modern systems create different types of content, such as text, images, code, or video, within a single multimodal system, each type still works using distinct approaches and training methods.

Text generation

Large language models represent the most capable machine learning systems in wide use today.

• What they do: They use natural language processing (NLP) and focus on teaching computers to understand and generate human language.
• How they work: Built on the transformer architecture, these systems understand the context and the relationships between words much like humans do.
• Why it matters: This breakthrough in NLP enabled much more accurate, context-aware language processing.

When you interact with systems like ChatGPT, you're using LLMs trained on billions of web pages, books, and articles, picking up grammar, tone, and specialized knowledge to handle any natural language query with fluency.

Image generation

Creating an AI-generated image might feel like magic, but sophisticated technology works behind the scenes to interpret your text prompt and generate the requested visual. These image generation models use advanced text encoders to understand your description, then employ architectures like diffusion models to create the actual image.

Code generation

AI code tools are trained on large libraries of open-source code, which teaches them to recognize programming patterns and the logic behind how developers solve problems. A user can describe what they need in plain English, and the output delivers working software code in programming languages such as Python or JavaScript.

Video and audio generation

AI can generate short videos by predicting how scenes should unfold from one frame to the next. It starts with a concept, like someone walking, and builds each step in the motion to create a smooth, natural sequence. In speech generation, AI takes written text and turns it into lifelike audio—adjusting for tone, pacing, and emphasis to match the rhythm and feel of real conversation.

What is the technology behind generative AI?

The capabilities of generative AI are built on a set of powerful technical building blocks. Let’s take a closer look at what’s going on behind the scenes.

Neural networks and deep learning

Neural networks are the engine behind most generative AI systems. Think of them as simplified versions of how the human brain works: information flows through layers of connected processing units called nodes, with each layer learning to recognize different patterns or features in the data. When a network has many layers, the approach is called deep learning.

During training, these networks convert different characteristics of data, like the color of a pixel in an image or the meaning of a word, into mathematical representations called vectors. By analyzing millions of examples, the network learns to identify patterns and relationships, then uses this knowledge to generate new, similar content.

Over time, different types of neural networks have been developed for different tasks:

• Recurrent neural networks (RNNs) were used in earlier systems to help AI work with things like speech and text. They processed one word at a time and carried information forward to understand what comes next.
  Example: A speech-to-text app that turns your voice into real-time captions by predicting each new word based on what you've just said.
• Convolutional neural networks (CNNs) specialize in analyzing images for computer vision tasks, detecting patterns like edges, shapes, and textures. 
  Example: A photo app that uses AI to detect and tag your friends' faces in a group picture.
• Variational autoencoders (VAEs) compress data into a compact mathematical form and then reconstruct it, learning to smooth out noise and generate new variations along the way.
  Example: A photo app that removes graininess from a low-light picture or creates subtle variations of an image.
• Transformer networks, now the foundation of modern generative AI models, process all parts of the input simultaneously, which makes them much better at understanding complex language and generating longer, more coherent responses.
  Example: An email app that suggests full-sentence replies, like "Sounds good, see you then," by instantly understanding the entire message.

How are generative AI foundation models trained?

Instead of training separate models for each task, foundation models like ChatGPT are trained to perform a variety of tasks, such as answering questions, writing code, and summarizing text.

Training these models requires significant resources, including massive compute power and carefully selected datasets.

Several types of data and feedback go into training them:

• Labeled and unlabeled data: A mix used in initial training, helping models generalize patterns across a wide range of content without direct supervision
• Synthetic data: Often used to supplement real datasets, especially when working with sensitive or limited information
• Human feedback: People rate or correct outputs, often called “reinforcement learning with human feedback”, or RLHF, to make responses more helpful, accurate, and aligned with what users expect

Once trained, organizations can adapt models in several ways:

• Fine-tuning them with their own data to perform specific tasks
• Adding custom knowledge bases
• Training additional layers that support their particular domain or use case
• Adding novel or complex data from sources like scientific papers, legal documents, and technical manuals

How is generative AI used across different industries?

Generative AI has been applied across multiple industries for content creation and automation—including healthcare for drug discovery and synthetic medical data; finance for report drafting and customer service; and media for music composition and video generation.

Here's how different industries are putting these tools to work:

• Healthcare: Healthcare providers use it to enhance medical imaging, assist with clinical documentation, and personalize treatment. It can also generate synthetic patient data for training machine learning models without exposing sensitive information.
• Drug discovery: Pharmaceutical and biotech companies use generative AI to speed early-stage drug development—identifying compounds and refining molecular structures. It can also help design clinical trials.
• Business process automation: genAI streamlines operations by automating repetitive tasks like drafting reports, summarizing spreadsheets, and answering customer inquiries—freeing teams for higher-value work.
• Creative industries and AI art: Designers and artists use tools like DALL-E, Midjourney, and Adobe Firefly to generate concept art and custom graphics from text prompts. genAI also composes music and creates environments, characters, and avatars for video games and simulations—turning work that once took hours into minutes.
• Software development enhancement: Generative AI helps developers write boilerplate code, refactor legacy systems, and create functions from plain-language prompts. Paired with platforms like MongoDB Atlas, it can also generate database queries, schema definitions, and application logic.
• Marketing and messaging tools: Marketing teams use generative AI to produce blog posts, ad copy, and email campaigns faster. Tools like Jasper, Copy.ai, and Persado can tailor the tone and format for different audiences.

What are the challenges in implementing generative AI?

Adopting generative AI can bring many challenges that go beyond setting up the model and getting it to perform. Some of those challenges include:

• Hallucinations: Models can produce outputs that sound plausible but are incorrect (hallucinations).
• Copyright: Training models on web-scraped and copyrighted material without permission has sparked legal disputes over fair use and creator compensation, and it remains unsettled whether AI-generated works can be copyrighted at all.
• Bias: Models can encode and amplify prejudices in their training data, producing unfair or discriminatory outcomes in areas like hiring and loan approvals, which is why diverse training data and ongoing evaluation matter.
• Misuse: Generative AI can be misused for deepfakes—simulating a person's appearance and speech in realistic AI-generated videos—that make it appear that someone said or did something they never did.

How do I add generative AI to my applications?

When you want to add generative AI to your applications, you’ll need to decide which approach matches your needs—implementing AI can be very simple, or very complex. On the easier end, you could add a chatbot to summarize emails. More complex options could involve fine-tuning an LLM, establishing agentic workflows, building a retrieval augmented generation system (RAG), or building your own vector stores with a vector database like MongoDB.

Key questions to help you decide include: Are you looking for a quick, simple solution using existing AI services? Or do you need something more sophisticated that works with your proprietary data and specific business logic?

Ready to dive in and start building on Atlas? Set up a cluster today or review our documentation.

If you’re looking to start from scratch or you’re not sure where to begin, take a look at our Building GenAI Apps Learning Badge Path or earn the RAG skill badge!

Want to explore our solutions? Take a look at AI use cases

Conclusion: Embracing generative AI

Generative AI is changing how we write, design, code, and communicate—shifting from analyzing information to creating it. But getting real value from it takes more than access: it requires understanding its limits, adding safeguards, and integrating it thoughtfully. As organizations navigate this shift, they should balance innovation with responsibility.