Generative artificial intelligence is a branch of AI capable of creating new content —text, images, audio, video, or code— from patterns learned from enormous amounts of data. Unlike traditional AI, which classifies or predicts, generative AI produces: it writes, draws, summarizes, and programs. Models like large language models (LLMs) and diffusion models are its greatest exponents.

In a few years, generative AI went from being a laboratory topic to being in the daily work of millions of people. Understanding what it is, how it works, and what it's for stopped being optional. This guide explains the fundamentals without unnecessary jargon and shows real use cases in companies in the region.

What it is and how it differs from traditional AI

"Classic" AI specializes in tasks like detecting fraud, recommending products, or predicting demand: it analyzes data and returns a classification or a number. Generative AI takes a step further: it generates original content that didn't exist before. According to IBM's definition, these models learn the structure of their training data and then produce new examples that follow that same structure.

How it works on the inside

Large language models (LLMs)

They are the base of assistants like ChatGPT or Claude. They are trained to predict the next word in a sequence. From that apparently simple ability emerges the capacity to converse, summarize, translate, and program.

Diffusion models

They are the ones that generate images. They learn to "clean" noise progressively until forming a coherent image from a text description.

Multimodality

The most recent models combine formats: they understand text, images, and audio at once, and they can respond mixing those formats. This opens up much richer use cases.

If you want to see in a practical way how far this technology reaches today, it's useful to complement with what generative AI can do today and how to start using it.

Real applications in LATAM companies

• Marketing and content: generation of texts, images, and campaigns at scale.

• Customer service: assistants that answer queries 24/7 with natural language.

• Software development: code assistants that speed up teams.

• Analysis and documentation: summaries of meetings, reports, and knowledge bases.

• Education and training: personalized materials and virtual tutors.

McKinsey's State of AI report shows that the functions where generative AI is adopted fastest are marketing, sales, product development, and service operations.

Limits and precautions

Generative AI doesn't "know" or "understand" like a person: it produces what's most probable according to its data. That's why it can be confidently wrong (the famous "hallucinations"), reflect biases from its data, and always need human supervision. Using it well means verifying, not delegating the judgment.

Recommended Coderhouse training

Understanding generative AI is the first step; applying it with judgment is what adds professional value. At Coderhouse there are options for different levels:

• Introduction to Artificial Intelligence Course: ideal for building the foundations from scratch.

• Content Creation with AI Course: to apply generative AI to text, image, and video.

• AI Engineering Course: for those who want to go deeper into the development of solutions with AI.

Start with the level that corresponds to you and turn generative AI into a concrete skill of your profile.

Frequently asked questions

Is generative AI the same as ChatGPT?

Not exactly. ChatGPT is an application of generative AI based on a large language model. Generative AI is the broader field that includes text, images, audio, video, and code.

Do I need to program to use generative AI?

Not to use it. The current tools are handled with natural language. Programming helps if you want to integrate it into systems or build your own solutions.

Can generative AI replace creative professionals?

It replaces repetitive tasks, not judgment. The professionals who use it as a tool produce more and better; human judgment remains the differentiator.

Why does generative AI sometimes make mistakes?

Because it generates what's most probable according to its data, without verifying facts. It can invent information with a credible appearance, which is why it's always a good idea to review its responses.