Generative AI: What It Is and How It’s Used

An assistant can draft an email in seconds and, in that same minute, make up a detail that never happened. Generative AI creates text, images, audio, video, and other content based on patterns learned from data. Understanding how it generates responses—and where it tends to fail—can help you make the most of its capabilities without trusting it blindly.

These systems already support tasks in creative work, research, programming, and education. But results depend on the model, the instructions, and the information provided. Human review is still essential, especially when financial, academic, or health-related consequences are involved.

In brief

Generative models create new content based on learned patterns, but they don’t guarantee that every claim is true.
Language, image, audio, and video models have different capabilities and limitations; some combine multiple modalities.
RAG connects a model to selected information sources, but it doesn’t eliminate errors or replace source verification.
Before using a tool, evaluate its privacy practices, references, cost, usage limits, and options for human review.

What generative AI is and how it works

Generative AI is a category of artificial intelligence systems that learn patterns in data and use what they’ve learned to produce new outputs, such as text, images, or audio. The model receives a prompt, calculates what output is likely given the context, and generates it step by step. The result may be useful and original, but that alone doesn’t prove the information is accurate.

During training, the system adjusts parameters to capture relationships between elements in the data. In a language model, for example, this includes patterns among words and passages of text. When someone enters a prompt, the model uses the available context to calculate a sequence of likely responses; it doesn’t automatically consult a universal database of facts.

This distinction explains a common mistake: treating a fluent response as if it were a verified source. If the model doesn’t have enough information, it may produce a plausible but incorrect claim. More specific instructions can help guide the response, but they don’t make the content infallible.

Fluency measures how well something is written; verification determines whether its content deserves your trust.

Models, modalities, and access to information

Text, images, audio, and video

Language models are trained to work with text and, in many cases, can also analyze other formats. Image models generate or edit visuals; audio models can transcribe, synthesize, or interpret speech. Video tools combine image and motion capabilities and sometimes sound. Capabilities vary across products and versions.

There are also open models, whose weights can be accessed under conditions set by their owners, and closed models, offered through services controlled by a company. “Open” doesn’t automatically mean unrestricted: licenses, infrastructure costs, training data, and technical requirements still matter. A closed service may be easier to use, but it offers less control over the model and how it works internally.

What RAG means

RAG, short for retrieval-augmented generation, is an architecture that searches selected documents or databases and provides that information to a model to help it compose a response. A company might use it to search internal manuals without training a model from scratch. Results depend on the quality, freshness, and relevance of the retrieved documents; a poor source can lead to a poor response, even with RAG.

Applications of generative AI in creative work, education, and the workplace

How predictive AI differs from generative AI

Predictive AI estimates a category, value, or probability based on data; generative AI produces new content based on what it has learned and the instructions it receives. A system that estimates the risk of a delivery being late is predictive. An assistant that drafts a message about the delay is generative.

Both approaches can appear in the same workflow. A tool might predict which requests need attention, then generate a draft response for the team to review. “Predictive” and “generative” describe different functions, not a single classification for all AI systems.

Other techniques, such as image classification or grouping records, don’t need to generate content either. Identifying the task helps you avoid choosing a generative model when a simple rule or classifier would be more appropriate, less expensive, and easier to audit.

Benefits, risks, and ethical considerations

Automated generation can reduce the time spent on first drafts and expand the range of alternatives explored. The actual benefit depends on how much rework is needed: if review takes longer than doing the task in the first place, automation hasn’t helped in that case.

Risks include factual errors, biases in the data, exposure of confidential information, misuse of copyrighted material, and the creation of misleading content. There are also security risks: an output may include inappropriate instructions or influence decisions without users recognizing its limitations.

The NIST AI Risk Management Framework offers guidance for identifying and managing risks throughout the life cycle of AI systems. UNESCO’s Recommendation on the Ethics of Artificial Intelligence addresses principles such as human rights, transparency, and human oversight. These are frameworks, not guarantees that a specific tool is safe or appropriate.

When children and teenagers use digital services, organizations and families should also consider applicable Brazilian law, including Law No. 15,211/2025, the Digital Statute for Children and Adolescents. How it applies depends on the context and the legal obligations involved; questions about the law call for consulting the official text and seeking qualified guidance.

How to choose tools, including free options

The best tool depends on the task, the data involved, and the level of control required. A free option may work for experiments with non-sensitive content, but pricing, limits, features, and availability vary by service, region, and plan. “Free” doesn’t mean unlimited use or guarantee that submitted data won’t be stored.

Type of tool	When to consider it	Access and limitations	Main consideration
Text assistant, such as Google Gemini	Drafts, explanations, and support with language tasks	Access and features depend on the product, account, and current terms	Check facts and don’t enter confidential data without understanding the service’s policies
Image generator or editor	Visual exploration, illustrations, and variations in composition	Resolution, generation limits, and usage rights may vary	Check policies on content, resemblance to people, and commercial use
Open model running locally	Projects that require more control over the environment and data	Requires installation, computing resources, and attention to licensing	Local hosting doesn’t eliminate security risks, bias, or errors

To compare Google AI, other online tools, or a local model, test each with the same task and clear criteria: response quality, available sources, privacy, total cost, and how easily errors can be corrected. If a system is connected to your own documents, also check whether you can trace each claim back to its source file.

For example, a team needs to summarize an internal procedure. First, it removes unnecessary personal data; then it tests the summary in an approved tool and checks each instruction against the original document. If the tool doesn’t explain how it handles files, the team should use an approved environment or choose another process.

Impact on work and best practices

Automation is more likely to change tasks before it eliminates entire professions: parts of writing, preliminary research, and information organization may change, while judgment, relationships, accountability, and contextual knowledge remain relevant. The impact varies by role, industry, system quality, and organizational decisions; sweeping predictions about jobs aren’t reliable.

Professionals who use these tools need to learn how to define tasks, evaluate outputs, and protect information. For work involving systems, for example, a documentation draft can speed up the process, but the team still needs to check requirements, architecture, and security. The same goes for decisions in trade, where regulatory and contractual information must be checked against appropriate sources.

Before adding an assistant to a workflow, define what data can be submitted, who reviews the output, and which errors would prevent publication. Also record when the system was used for higher-impact decisions. For teams working with systems, this helps distinguish an automated suggestion from an approved change.

The next step is to choose a small, reversible task that involves no personal or confidential data and compare the result with the current process. If the tool saves time without reducing quality and allows for adequate review, move on to a controlled test; otherwise, adjust the workflow or stop using it.

Frequently asked questions

Is Google Gemini generative AI?

Yes. Gemini is a family of models and also the name of Google’s AI products. Features, access options, and terms of use may vary; check the service’s current information before submitting files or personal data.

Does a well-written response mean it’s correct?

No. Models can generate false claims in a confident tone, especially when context is missing or a question asks for highly specific details. Check important information against original documents or reliable sources.

Can I submit work documents to a free tool?

Only after checking the service’s privacy, data retention, and data use policies, as well as your organization’s policies. If a document contains personal, business, or confidential information, use only a tool approved for that type of data.

Are open models always safer?

No. They may offer more control over installation and configuration, but security depends on how they’re hosted, updated, and protected. You also need to check the license and the data the model processes.

Do I need to disclose when content was created with AI?

It depends on the context, institutional rules, and applicable regulations. For academic work, advertising, and content that may influence decisions, checking transparency requirements before publication can prevent problems and preserve the audience’s trust.

Generative AI: Transforming Digital Creation and Beyond