In recent years, the conversation around technology has been dominated by acronyms, proper names, and concepts that are often used as synonyms, even when they represent very different things.

Everything became "artificial intelligence." Simple automation tools began to be presented as advanced AI, brands became category benchmarks, and technical terms started circulating outside the academic or corporate environment.

ChatGPT, Gemini, Copilot, Claude. Deep learning, machine learning, LLMs, prompts.

For those who follow the subject closely, this vocabulary is part of daily life. For those who are just starting, or even for marketing, communication, product, and business professionals, understanding the true meaning of these terms is essential for making more conscious decisions, avoiding hype, and differentiating technology from marketing discourse.

What is Artificial Intelligence, really?

Artificial intelligence (AI) is an umbrella term.

It encompasses a broad set of techniques, models, and systems capable of performing tasks that, until recently, required some level of human intelligence, such as pattern recognition, decision-making, language understanding, and learning from data.

Within this umbrella are different approaches, levels of complexity, and applications. Not all AI generates text, images, or video. Not all AI learns on its own. And not all automation is, in fact, artificial intelligence.

It is important to separate the technical concept from the market narrative. The history of AI begins in the 20th century, with research in logic, mathematics, and computer science. The most visible advancements today are the results of combining greater computational power, massive volumes of data, and new model architectures.

It is also worth highlighting the phenomenon known as AI washing: when companies start using the term "artificial intelligence" in an exaggerated or imprecise way to promote products that have little or no AI. Understanding the concepts helps precisely in identifying this type of practice.

Machine Learning, Deep Learning, and Neural Networks

Most modern AI applications rely on Machine Learning. This is a subfield of artificial intelligence focused on developing algorithms capable of learning patterns from data, without being explicitly programmed for each decision.

Within machine learning, there is Deep Learning, a more advanced approach based on artificial neural networks. These networks are inspired, in a simplified way, by the functioning of the human brain, with layers of artificial neurons that process information.

Deep learning has enabled significant breakthroughs in areas such as:

• Image recognition (computer vision).

• Speech recognition and synthesis.

• Machine translation.

• Natural language processing.

The more layers a neural network has, the more complex the learned patterns can be. In contrast, the computational, energetic, and financial cost involved in training and operating these models is also higher.

Language Models: LLMs, SLMs, and GenAI

When we talk about tools like ChatGPT, Gemini, or Claude, we are dealing with Language Models.

Large Language Models (LLMs) are large-scale models trained on massive volumes of text to understand and generate natural language. They work by predicting the next word (or part of a word) based on context, utilizing billions — or trillions — of parameters.

Examples of the most well-known LLMs:

• ChatGPT (OpenAI).

• Gemini (Google).

• Copilot (Microsoft).

• Claude (Anthropic).

On the other hand, Small Language Models (SLMs) are more compact versions, designed for specific tasks. They require fewer computational resources and can be more efficient in well-defined contexts, such as internal customer service, document analysis, or embedded applications.

Within this group is Generative Artificial Intelligence (GenAI), capable of creating new content (texts, images, videos, audios, or code) based on user prompts.

ChatGPT is not synonymous with AI

ChatGPT has become, for many, synonymous with artificial intelligence. In practice, it is a specific product created by OpenAI, a company led by Sam Altman and backed by Microsoft.

Just as "Google" became synonymous with search, ChatGPT came to represent an entire category. But the ecosystem is much broader.

Other relevant examples:

• Claude, by Anthropic, with a focus on safety and alignment.

• Copilot, by Microsoft, integrated into products like Windows, Office, and GitHub.

• Gemini, by Google, present in search, Android, and enterprise solutions.

• Perplexity, a search engine based on conversational answers.

Understanding the difference between brand, model, and application helps to better evaluate the capabilities, limitations, and uses of each tool.

Learn more: DeepSeek, ChatGPT, Gemini, or Copilot: which is the best choice for your project?

Read also: Claude: the artificial intelligence that conquered programming

Prompts, tokens, and fine-tuning

Interaction with generative models happens through prompts. A prompt is the command or instruction provided to the model, indicating what it should do.

The quality of the response is directly linked to the clarity, context, and structure of this command. Because of this, the term prompt engineering has emerged, focused on creating more effective instructions.

Another important concept is the token. Tokens are the minimal units of text processed by the model and can be entire words or parts of them. They serve as the basis for measuring input limits, output limits, cost, and performance.

Fine-tuning, on the other hand, is the process of adjusting a previously trained model for a specific task or domain using an additional set of data. This allows for responses more aligned with a business context, technical language, or editorial standard.

Artificial voice, neural voice, and deepfakes

Audio generation by AI has evolved rapidly. Traditional artificial voice uses rules and basic synthesis, resulting in more robotic sounds. Neural voice, however, is created from neural networks trained on large volumes of human recordings, generating more natural intonation, pauses, and variations.

Platforms like ElevenLabs have popularized this type of technology, allowing the creation of realistic voices from text.

The same technical advancement made deepfakes possible — audio or video content manipulated to simulate real people. While they have legitimate uses in entertainment and education, deepfakes also raise concerns related to fraud, disinformation, and the misuse of image rights.

Understand the details: Audiovisual production with AI: ethics, authorship, and limits in the era of deepfakes

Hallucination, biases, and synthetic data

One of the most well-known challenges of generative AI is hallucination: when the model presents false or inaccurate information as if it were true.

This happens because the model does not "know" facts; it predicts patterns based on training data.

Another critical point is biases, inherited from the data used in training. They can reinforce stereotypes or distortions that exist in society.

To bypass the limitations of real data, the use of synthetic data has been growing — artificially generated information designed to simulate real scenarios, while respecting privacy and expanding possibilities for testing and research.

Open source and AI agents

Open-source AI refers to models and tools whose code or weights are publicly available, in varying levels of transparency.

Meta, Google, and other companies adopt this model to accelerate innovation and adoption.

Another expanding concept is AI agents: systems capable of executing tasks semi-autonomously, combining planning, tool usage, and decision-making.

These agents point to a more integrated use of AI in daily work, going beyond one-off answers.

The vocabulary of artificial intelligence is growing at the same pace as the technology.

Understanding the true meaning of each term is an important step toward using AI with greater awareness, a critical eye, and efficiency.

As technology begins to organize processes, products, and decisions, it also begins to shape discourse, creating a new language that influences how companies, professionals, and users think and speak about innovation.

To understand the terms is to understand the very debate around artificial intelligence.