Skip to main content

What is Generative AI in Simple Terms?

Catherine Canary, Appian
March 28, 2024

Generative AI is the kind of AI you can use to create new text, visual, and audio content. It’s not a new concept, but it’s been newly simplified and made accessible to the average user. Now, anyone can use generative AI to massively speed up content creation tasks. 

Let’s talk about how it works.

[Generative AI is just one trend in the realm of AI. Find out what else IT leaders need to know to keep up with the competitive AI landscape. Download the 2024 AI Outlook.]

Definition of generative AI and key concepts.

Generative AI is a type of artificial intelligence used to create new content based on models trained on text, visual, and audio data in response to prompts. Traditional AI systems rely on pre-existing data and patterns to perform tasks, making generative AI’s ability to generate new content unique. 

Generative AI uses algorithms and models, such as large language models (LLMs), to learn patterns from a dataset and then generate new data that follows those patterns. These algorithms are designed to understand and mimic the underlying structure and characteristics of the input data, enabling them to produce outputs that are similar but not identical to the training data. Generative AI tools have revolutionized fields like process automation and business process management across industries, and beyond that there’s a wide-range of possibilities yet to be explored.

Artificial Intelligence

At its core, artificial intelligence (AI) encompasses the development of computer systems capable of performing tasks that typically require human intelligence. Generative AI represents a specialized field within AI that focuses on the creation of new content.

Generative Models

Generative models are algorithms employed in generative AI to generate new data points. These models learn from a given dataset and then produce outputs that resemble the original data.

Neural Networks

Neural networks are a fundamental component of generative AI. Inspired by the structure of the human brain, neural networks consist of interconnected nodes, or "neurons," that process and analyze data to identify patterns.

Adversarial Networks and Discriminator Networks

An adversarial network refers to a neural network architecture used in generative adversarial networks (GANs). Adversarial networks consist of two competing neural networks: the generator and the discriminator. The generator aims to produce realistic data, while the discriminator evaluates the generated data's authenticity. Through an adversarial training process, the generator aims to produce data that is indistinguishable from real data, while the discriminator learns to distinguish between real and fake data.

Deep Learning

Deep learning techniques, including neural networks with multiple layers, are a type of machine learning crucial for training generative AI models. Deep learning algorithms enable AI tools to find patterns in text, visual, and auditory data that they can use to deliver insights and make predictions.

Transformer Architecture

Transformer architecture is a type of deep learning model used in generative AI natural language processing. It processes input data all at once, capturing relationships between words efficiently. It consists of an encoder and a decoder. The encoder converts input data into vectors, considering the context of each word. The decoder generates an output sequence based on the encoded representations. The GPT in ChatGPT stands for Generative Pre-trained Transformer.

Generative AI vs. Private AI: What’s the difference?

While generative AI focuses on content generation, a private AI approach prioritizes data privacy and security in AI applications. While they serve different purposes, both approaches play critical roles in advancing AI technologies and addressing the diverse needs and challenges of modern businesses and industries.

[Looking for guidance on implementing AI in a way that is safe and secure? Check out the Private AI guide.]

Benefits of generative AI.

The main benefit of generative AI is speed. When an AI is creating your content for you, or at least getting you to a good starting point, your process will go much more quickly. Here are some ways you can create more speed with GenAI:

Draft email responses automatically.

Generative AI can draft email responses automatically, saving valuable time spent crafting individual replies. With the ability to understand context and tone, AI-generated email responses can maintain professionalism while expediting communication.

Summarize documents in seconds.

Long documents or sets of records can be condensed into concise summaries within seconds using generative AI. This capability is particularly useful for quickly extracting key information from lengthy texts, enabling efficient information processing and decision-making.

[Appian AI Skills includes GenAI features to draft emails, summarize documents, and more, automatically. Watch this video to find out more.]

Build more advanced chatbots to speed customer service responses.

Generative AI enables the development of more advanced chatbots capable of providing rapid and accurate responses to customer inquiries. By leveraging natural language processing capabilities, these chatbots streamline customer service interactions, reducing response times and improving overall customer satisfaction.

Limitations and ethical concerns.

Generative AI offers numerous benefits, including the creation of realistic images, text, and other media. However, it also raises ethical concerns related to misinformation, privacy infringement, and bias in generated content. Striking a balance between innovation and responsible use is essential in harnessing the full potential of generative AI. 

This is why human oversight of AI is critical. AI tools can create outputs that either contain harmless bias or misinformation in the form of AI hallucinations. You’ll want to have a human in the loop to review AI-created content to avoid the embarrassing and sometimes harmful incident of publishing this content. Prompt engineering is another way to minimize poor AI outputs. Prompt engineering is a technique used to guide the output of language models and shape AI-generated content toward desired outcomes.

Concerns have also been raised over the tendency of AI to fail to cite the sources it used to generate content, leading to human creators not being credited for the work used by AI.

Examples of generative AI technologies.

ChatGPT: ChatGPT, developed by OpenAI, is an advanced conversational AI model capable of generating human-like responses in text-based communication. It demonstrates the potential of generative AI in natural language processing tasks, illustrating the effectiveness of using language models in virtual assistants.

DALL-E: DALL-E, another creation from OpenAI, showcases the ability of generative AI to generate images from textual descriptions. This innovative model opens up possibilities for generating custom visuals based on user input, highlighting the versatility of diffusion models in image generation.

Google Gemini: Google's Gemini project also explores the application of generative AI in image synthesis and manipulation. By leveraging advanced generative models, Gemini enables the creation of high-quality images with diverse styles and characteristics, showcasing the potential of AI-generated content in visual media.

Real-world applications of generative AI.

Generative AI, with its ability to create new content based on learned patterns, has found diverse applications across industries:


Generative AI accelerates drug discovery by generating molecular structures and aids in medical imaging for diagnosis and treatment planning.

Virtual Assistants

AI-driven chatbots and voice assistants enhance customer service, digital marketing, and personal productivity with natural language interactions.

Content Creation

AI automates content production in journalism, marketing, and social media, from news articles to social media posts and advertisements.

Education and Research

Generative AI facilitates personalized learning experiences and aids researchers in data synthesis, hypothesis generation, and simulation.

Finance and Business

AI-driven forecasts and analyses support financial modeling, risk assessment, and decision-making in finance and business.

Generative AI can be used across virtually every industry for countless use cases.

The future of generative AI.

As generative AI continues to advance, its impact on various industries, including entertainment, healthcare, and design, will become more pronounced. The development of more sophisticated algorithms and ethical frameworks will shape the responsible integration of generative AI into society, unlocking new avenues for innovation and creativity. With ongoing advancements in prompt engineering and generative AI tools, the future holds immense potential for the widespread adoption of AI-generated content across a wide range of applications.

[AI is advancing rapidly, and there’s so much more to it than just genAI. Check out the 2024 AI Outlook to find out what IT leaders need to know to stay competitive in the AI age.]