Module 1 · Explore

What Is Generative AI?

Generative AI (GenAI) refers to algorithms that can create new digital content: text, images, video, music, and computer code. Unlike traditional AI that focuses on a single specific task, GenAI produces new content based on patterns learned from large datasets during a process called training.

Generative AI in Everyday Life

Generative AI is changing how we work, create, and interact with technology. Here are some common ways you might encounter it:

Writing assistance: drafting e-mails, reports, and outlines with tools like Grammarly or Goblin Tools.
Image creation: making images from text prompts with tools like Midjourney or DALL-E.
Music and video generation: AI can generate video (for example, Synthesia) and even full songs. AI music is especially contested, though: major music companies have sued leading song generators for training on artists' recordings without permission, so it is an area to approach thoughtfully.
Software development: writing and debugging code with tools like GitHub Copilot.

A note on tools: mentioning a tool here is not an endorsement, and "ethically trained" claims deserve scrutiny. Adobe Firefly, for example, advertised that it was trained only on licensed and consented work, but was later found to have used AI-generated stock images. Because of issues like this, Better Images of AI (the source of this course's artwork) accepts no current image generator as meeting its standards. The lesson: ask how a tool was actually built rather than taking the marketing at face value. And not every tool is approved for your (YOUR CAMPUS) account or safe for personal data, so check the ITS AI Guide before signing in.

Contested ground: many of these tools are the subject of active lawsuits and artist protest. In 2024, more than 200 musicians, including Billie Eilish and Stevie Wonder, signed an open letter against AI music generators, and major record labels sued the song generators Suno and Udio for training on recordings without permission. Visual artists and news organizations have filed similar suits over images and text. These fights are still unresolved, and you will look at them closely in Module 2.

How Does Generative AI Work?

GenAI models create content by analyzing patterns in data and responding to user prompts, the instructions that guide the model's output. A prompt can be as simple as "Summarize this article" or as detailed as "Generate an image of a futuristic city at sunset."

One widely known example is ChatGPT, a large language model (LLM) that produces human-like text. When given a prompt, an LLM:

Analyzes its training data, which might include blog posts, government websites, books, articles, and code repositories.
Predicts the most likely next words or sentences based on learned language patterns.
Generates a contextually relevant response.

Watch: How Chatbots and Large Language Models Work

Key insight: LLMs like ChatGPT generate content by recognizing patterns in language, but they lack true understanding of meaning. That is why they can sound confident and still be wrong.

Strengths and Limitations

GenAI is powerful, but using it well means recognizing both what it does well and where it falls short.

Strengths

Generates content in many formats, including text, images, and code.
Automates repetitive tasks, which can improve productivity.
Supports creative workflows with idea generation, variations, and drafts.

Limitations

Can produce biased or inaccurate content when trained on flawed data.
Lacks true understanding; outputs come from patterns, not comprehension.
Uses significant energy and water, which makes training and running these models costly.

Knowledge Check

Select an answer to see feedback. Each option explains why it is or is not correct.

Question 1 of 2

What makes generative AI different from traditional AI models?

Question 2 of 2

A large language model like ChatGPT generates text mainly by: