Artificial intelligence: a complete guide to understanding and using it

Artificial intelligence is already part of numerous tools and services that we use daily. Recommendation systems, conversational assistants, image recognition, automatic translators, and applications capable of creating text, video, or images are some examples.

However, AI is not a single technology nor does it always work the same way. There are systems designed to predict results, classify information, generate content, recommend products, recognize patterns, or perform certain tasks automatically.

In this guide you will learn what artificial intelligence is, how it works, what types exist, where it is used, what its main benefits and limitations are, and how you can start taking advantage of it without having to be a programmer.

What is Artificial Intelligence?

Artificial intelligence is a field of computer science dedicated to developing systems capable of producing results that we associate with certain intelligent capabilities.

An AI system can use the information it receives to generate:

• Predictions.
• Classifications.
• Recommendations.
• Content.
• Answers.
• Decisions.
• Actions within a physical or digital environment.

The OECD defines an AI system as a machine-based system that infers, from received inputs and explicit or implicit objectives, how to generate results that can influence physical or virtual environments. Systems can vary in their level of autonomy and their ability to adapt after deployment.

This does not necessarily mean that the machine thinks or understands like a person. A system can perform a complex task by analyzing patterns and calculating results without possessing human awareness, intention, or understanding.

In our guide about what artificial intelligence is and how it works You will find a more detailed explanation of data, algorithms, training, models, and neural networks.

How artificial intelligence works

How it works depends on the system used, but many AI projects follow a similar process:

1. They receive information.
2. They process or prepare the data.
3. They use a model to find patterns.
4. They generate a result.
5. They evaluate the result and make adjustments where appropriate.

Input data

Data is the information used by the system.

They may consist of:

• Texts.
• Images.
• Audio recordings.
• Videos.
• Numerical data.
• Behavioral histories.
• Information from sensors.
• Instructions written by the user.

The quality, relevance and representativeness of the data directly influence the behavior of the system.

Having a large amount of information does not guarantee good results. The data must also be correctly selected, organized, and reviewed.

Model training

During training, the system adjusts its parameters to learn relationships and patterns present in the data.

For example, a model designed to recognize images can analyze numerous labeled examples until it learns which features help to differentiate some categories from others.

The process changes depending on the technique used. Some models learn through labeled examples, others look for structures without labels, and others improve through rewards associated with their actions.

Generation of results

Once trained, the model can receive a new input and produce an output.

That solution could be:

• A category.
• A probability.
• A forecast.
• A recommendation.
• A written response.
• An image.
• An action.

When you use a conversational assistant, for example, your instruction is processed as input and the model generates a response based on the patterns learned and the available context.

Evaluation and supervision

Models should be evaluated to verify their accuracy, reliability, safety, and suitability for the task.

Human supervision may also be necessary for:

• Review important results.
• Detect errors.
• Correct biases.
• Update data.
• Manage unexpected cases.
• Prevent the system from being used outside its purpose.

Artificial intelligence should not be automatically considered correct just because its answer seems convincing.

Differences between AI, Machine Learning and Deep Learning

Although they are often used synonymously, they represent related but different concepts.

The relationship can be understood as follows:

    Artificial intelligence
└── Machine Learning
  Deep Learning

Every Deep Learning system is part of Machine Learning, and this falls within the general field of artificial intelligence. However, not all artificial intelligence uses machine learning or deep neural networks.

What types of artificial intelligence exist

AI can be classified using different criteria. There is no single list valid for all contexts.

AI according to its capabilities

A common classification distinguishes between:

• Narrow artificial intelligence or ANI: designed to perform certain tasks.
• Artificial general intelligence or AGI: concept of an AI capable of generally performing in a wide variety of intellectual tasks.
• Artificial superintelligence or ASI: hypothetical scenario in which a system would far exceed human capabilities.

The AI we currently use is primarily made up of specialized systems, although some models can perform an increasing variety of tasks. AGI and ASI continue to be concepts of research and debate, and not product categories that are clearly achieved and generally accepted.

AI according to the task it performs

We can also differentiate:

• Predictive AI: calculate what might happen.
• Classifying AI: assigns information to categories.
• Recommendation AI: select content or products.
• Conversational AI: interacts through language.
• Artificial vision: Analyze images and videos.
• Generative AI: produces new content.
• Autonomous systems: They perform actions with certain degrees of autonomy.
• AI Agents: They combine models, instructions, and tools to complete objectives.

Our guide to the types of artificial intelligence It develops the differences between ANI, AGI, ASI, predictive, generative, conversational AI and other approaches.

Main applications of artificial intelligence

The usefulness of AI does not depend solely on one sector, but on the problem being solved.

AI is also used daily in search engines, spam filters, translation, facial recognition, voice assistants, and recommendation systems.

The guide on applications of artificial intelligence It analyzes in greater depth its uses in education, business, health, marketing, industry and other sectors.

You also have specialized guides on:

• Artificial intelligence for students.
• Artificial intelligence for businesses.
• AI applied to marketing.
• Artificial intelligence for SEO.

What is generative artificial intelligence

Generative AI is designed to produce content from instructions, examples, or other input data.

It can generate:

• Text.
• Images.
• Audio.
• Music.
• Video.
• Code.
• Presentations.
• Three-dimensional models.

These systems learn relationships present in the data used during their development and generate results that follow similar patterns.

Generative AI does not necessarily retrieve an exact stored response. Depending on the system, it constructs a new result from probabilities, learned representations, and the context provided.

This explains why the same instruction can generate different responses and why the system can also produce errors, non-existent details, or content that needs revision.

Check out our guide on what is generative AI to learn about their models, formats, applications, advantages and risks.

Artificial intelligence tools

There is no single tool that is best for all tasks. The choice depends on the objective, the work format, the necessary integrations, and the level of control you need.

These names are examples, not a definitive classification. Features, prices and capabilities change rapidly, so comparisons should be reviewed periodically.

You can consult:

• The best artificial intelligence tools.
• The comparison between ChatGPT, Gemini and Claude.
• Our guide to creating images with artificial intelligence.

Advantages of artificial intelligence

AI can add value when applied to a suitable problem and monitored correctly.

Task automation

It allows you to reduce the time spent on repetitive tasks such as sorting, data extraction, preparing drafts, or processing frequent queries.

Analysis of large amounts of information

Models can detect relationships and patterns in volumes of data that are difficult to review manually.

Personalization

AI allows recommendations, explanations, products or experiences to be adapted to different profiles.

Support for decision making

It can provide predictions, rankings, or relevant information so that a person can make a better-informed decision.

Accessibility

Speech recognition, transcription, translation, and description generation can facilitate access to information and services.

Creativity and productivity

Generative systems can help develop ideas, drafts, designs, code, and initial materials that a person will later review.

Risks and limitations of artificial intelligence

AI also has limitations that must be understood before being used in important tasks.

Incorrect information

A system can generate a convincing but incorrect response, especially when it does not have enough information or is asked for something beyond its capacity.

Biases

If the data or development process contains imbalances, the model may reproduce unfair or unrepresentative results.

Privacy

Entering personal, business or confidential information into an external tool can generate risks if its conditions of use and data processing are not known.

Lack of transparency

Some complex models make it difficult to understand exactly why a certain result has occurred.

Excessive dependence

Delegating tasks without reviewing the results can deteriorate one's own judgment and multiply errors.

Malicious use

AI can be used to create disinformation, impersonation, misleading content, automated attacks, or manipulation.

NIST recommends managing AI risks throughout their lifecycle, taking into account reliability, security, privacy, transparency, bias, and oversight.

The rule of thumb is simple:

The greater the impact of a decision, the higher the level of human verification and supervision should be.

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How to start using artificial intelligence

You don't need to learn programming to get started. The important thing is to choose a specific task and check if the tool really improves the process.

Define a specific task

Avoid starting with too broad a goal like “I want to use AI”.

Choose a task:

• Summarize a text.
• Create a draft.
• Organize information.
• Prepare questions.
• Compare options.
• Generate ideas.
• Analyze a document.
• Create an initial image.

Choose a tool

Select a platform appropriate to the format:

• Conversational assistant for text.
• Visual generator for images.
• Specialized video tool.
• Programming copilot for code.
• Automation platform for processes.

Learn to provide context

Good instruction should explain:

• What you need.
• What you need it for.
• Who will use the result.
• What information you should take into account.
• In what format should you respond.
• What limitations you must respect.

The guide of prompts for ChatGPT It contains structures and examples that you can adapt.

Check the results

Check:

• Data.
• Names.
• Dates.
• Sources.
• Calculations.
• Interpretations.
• Possible omissions.
• Sensitive information.

Do not automatically publish or use an important result without reviewing it.

Integrate AI progressively

When a simple task works well, you can incorporate it into a broader process.

For example:

1. Research a topic.
2. Organize the data.
3. Create a draft.
4. Review it manually.
5. Adapt it to the user.
6. Publish the final version.

For repetitive processes you can consult our guide on automation with artificial intelligence.

Those starting out with conversational assistants can also follow the tutorial on How to use ChatGPT step by step.

How artificial intelligence can evolve

Systems capacity continues to advance in areas such as language, programming, reasoning, multimedia generation, science, and agent task execution.

However, it is not possible to predict with certainty what capabilities will appear, when they will be available, or what impact they will produce.

Evolution will likely be marked by:

• Models capable of working with various formats.
• Agents that use tools and complete processes.
• Greater integration into devices and applications.
• Systems specialized in specific sectors.
• More automation.
• Greater need for evaluation and regulation.
• New forms of interaction between people and machines.

Stanford's AI Index 2026 highlights both the rapid advancement of capabilities and adoption and the difficulties in measuring, managing and governing that evolution at the same pace.

Learning to use AI critically will be more useful than trying to guess exactly what it will be like several years from now.

Conclusion

Artificial intelligence brings together different technologies capable of generating predictions, recommendations, classifications, content or actions based on the data and objectives received.

It is not a single tool nor an intelligence equivalent to human intelligence. There are specialized systems in language, images, recommendation, prediction, automation, and many other tasks.

To use it correctly you must choose a specific application, provide sufficient context, protect sensitive information and always check important results.

Frequently asked questions about Artificial Intelligence