Tilda Education
How To Understand & Use AI To Its Full Potential
A beginner's guide to AI tools: Different types, how they work, and tips to maximize results
Apr. 16, 2026
18 min
Contents:
What Is Artificial Intelligence In Simple Terms
Artificial intelligence, or to be more specific, a neural network, is a system that can independently search for and analyze information, generate ideas, and solve many different tasks. For example, it can create text, images, videos, and even music on its own.
Neural networks differ from traditional computer programs because they aren't built around a fixed set of prewritten answers. Instead of following strict instructions, AI systems learn how to interact with people and perform tasks in a way that's somewhat similar to how students learn in school: They study examples, make mistakes, and try again. This means developers don't have to program every possible action or manually add new information each time. As the system processes more data, it gradually improves.
In everyday conversations, people often use the terms "neural network" and "artificial intelligence" interchangeably, but they aren't exactly the same thing. Artificial intelligence (AI) is a broad term for technologies that help computer systems make decisions, sometimes using strict rules and sometimes relying on data and statistics. A neural network is one specific type of AI technology: A model that learns from examples.
How Artificial Intelligence Works
AI models operate through complex processes. In this section, we'll explain the basics in simple terms with clear examples.
1
Neurons
The human brain is made up of specialized cells called neurons. These cells allow the brain to function. Without them, it would simply be a cluster of biological tissue. Neurons receive and transmit signals from the outside world, from sensory organs, and from other neurons.
Neural networks have their own version of neurons but instead of biological cells, they are mathematical formulas. Unlike humans, AI systems don't understand language, images, or sounds directly. They work with numbers. When we send a request to a neural network, the system first converts it into numerical data. In other words, it translates the input into a format it can process. Once the request is converted into numbers, the system begins analyzing it.
2
Neural Connections
In the human brain, neurons are connected through pathways called neural connections. These connections form when certain neurons are activated together repeatedly. When we learn new things or practice skills, these connections grow stronger. The more often the same process repeats, the stronger the connection becomes.
Take brushing your teeth in the morning as an example. When a child first learns this routine, different neurons activate to process information about the toothbrush, toothpaste, hand movement, and other actions. Over time, these neurons begin to work together consistently, forming a stable connection. Since the action is repeated every day, the neural pathway becomes so strong that you can eventually brush your teeth almost automatically, sometimes before you're fully awake.
Artificial neural networks work in a similar way. During training, the model gradually forms connections between its mathematical neurons. These neurons start transmitting signals to one another. The more often the system encounters the same type of task, the stronger these connections become. As a result, the neural network becomes faster and makes fewer mistakes.
3
Internal Weights
Human neurons receive many signals, but they only pass along the strongest ones. For example, our senses constantly receive huge amounts of information: Sounds, smells, and visual signals. However, the brain doesn't process everything equally. Stronger signals receive more attention.
Imagine walking down a busy street. You may not notice every smell in the air, but you immediately recognize the familiar scent of fresh bread from a nearby bakery. In that moment, the smell receptors send a strong signal to the brain.
Imagine walking down a busy street. You may not notice every smell in the air, but you immediately recognize the familiar scent of fresh bread from a nearby bakery. In that moment, the smell receptors send a strong signal to the brain.
Neural networks operate using a similar principle. To determine which information is important, the system uses internal weights. When a signal passes through a neuron (a mathematical formula), it is multiplied by a weight that represents its importance. If the weight is high, the signal becomes stronger. If the weight is low, the signal is mostly ignored.
If a user types "Hi!" as a prompt, AI doesn't just dump everything it knows about greetings—it evaluates the information it has to determine what's actually relevant for that specific response.
A weight is simply a number that shows how important a specific input is. During training, neural networks constantly adjust these weights while processing massive amounts of data.
A Simple Example
Imagine a person asks an AI system: "A beautiful cat is sleeping on the couch. What do you think about it?"
The neural network first breaks the request into words, such as "beautiful," "cat," "sleeping," and "couch." Next, it converts these words into numbers and processes them using mathematical neurons. The system then determines how much attention to give each word based on its weight.
For example:
Words with higher weights have a stronger influence on the response, while words with lower weights play a smaller role. This means the neural network doesn't truly understand the meaning of the sentence. Instead, it identifies which parts of the input are most important based on statistical patterns. In this example, the system interprets the request as primarily about the cat, while the other details are less important.
The neural network first breaks the request into words, such as "beautiful," "cat," "sleeping," and "couch." Next, it converts these words into numbers and processes them using mathematical neurons. The system then determines how much attention to give each word based on its weight.
For example:
- "Cat" may receive a high weight because it's the main subject.
- "Couch" might receive a medium weight.
- "Beautiful" might receive a smaller weight.
Words with higher weights have a stronger influence on the response, while words with lower weights play a smaller role. This means the neural network doesn't truly understand the meaning of the sentence. Instead, it identifies which parts of the input are most important based on statistical patterns. In this example, the system interprets the request as primarily about the cat, while the other details are less important.
4
Generating a Response
Finally, the neural network begins generating its response. It doesn't choose words randomly. Instead, it selects words and phrases that statistically appear most often alongside the patterns it identified in its training data. In other words, the response is based on probabilities, the phrases that are most likely to appear in similar situations.
The information collected and generated in the previous stage is then analyzed again, and the AI determines the exact wording it should use in its response.
Developers also build safety rules into AI systems. These include restrictions on certain topics, as well as guidelines related to ethics and responsible behavior. For this reason, AI responses can sometimes seem as if the system is thinking, expressing opinions, or following personal principles. In reality, it is simply following patterns from data and the rules defined by its developers.
How AI Models Learn
Neural networks learn in a way that's somewhat similar to humans: Through examples and mistakes.
Here's how the process typically works:
- The system is shown a large number of examples. For instance, thousands of photos of cats and dogs.
- The model analyzes the first image and tries to guess what it shows. It might say: "This looks like a cat."
- A human (or another system) tells the model whether the guess was correct. If the suggestion was wrong, the neural network records the mistake.
- Next, the model adjusts its internal settings, specifically, the weights. For example, when analyzing a photo of a cat, the model might increase the weight for features like cat ears, while decreasing the weight for features associated with dog tails. Over time, the system learns which signals are important and which can be ignored. Even if the model notices something that looks like a dog's tail in a photo of a cat, this time it will give it minimal significance.
- The system repeats this process millions of times. The more examples and attempts it processes, the better the neural network becomes.
Why Use AI Tools
Save time and effort. Different AI tools can handle routine tasks such as searching for information, drafting text, generating ideas, processing data, and creating images, audio, or video. Tasks that once took hours can now be done in minutes.
Expand creative possibilities. Even without specialized skills, people can create text, images, music, scripts, presentations, and more. You no longer need professional tools, large budgets, or industry connections to start creating.
All of these images are AI-generated.
Gain a competitive advantage at work. People who know how to use AI tools often work faster and more efficiently. In many fields, working with AI is quickly becoming a basic skill, much like knowing how to use a computer or the Internet once was.
Support business and projects. AI can help test ideas, analyze markets, write texts, build marketing, write marketing copy, generate visuals, and automate routine processes with minimal cost.
This is especially valuable for small businesses and startups, particularly for founders who may not yet have large budgets or extensive experience. At the same time, established companies can also benefit from integrating AI tools into their workflows, many businesses already use them to increase efficiency and scale their operations.
The trend is on the rise—classic photoshoots with real models are now often being replaced by AI-generated ones.
The trend is on the rise—classic photoshoots with real models are now often being replaced by AI-generated ones.
Prepare for the future. AI is already integrated into banking, search engines, education, healthcare, and many digital services. The ability to work with AI tools is becoming an increasingly valuable skill.
When you google something, the first thing you'll see is not actual links but a so-called "AI overview," an internal Google tool that analyzes different sources and summarizes them.
What Tasks AI Tools Can Handle
Text
Text capabilities:
- Answer questions
- Explain complex information in simple terms
- Write and continue text
- Translate between languages
- Summarize long texts
- Check spelling and grammar
- Identify logical errors or misleading information
Images & Video
Image capabilities:
- Recognize people, animals, and objects
- Detect and recognize faces
- Estimate a person's age or emotional expression
- Find similar images
- Improve photo quality by removing noise or blur
- Restore old or damaged images
Video capabilities:
- Analyze the behavior of people and objects
- Recognize actions in a video
- Locate specific moments in long recordings
- Search for videos using text descriptions
- Automatically create short highlight clips
- Improve video quality and stabilization
Sound & Speech
Sound and speech capabilities:
- Recognize spoken language and convert it into text
- Turn text into natural-sounding speech
- Distinguish between different voices
- Identify the language someone is speaking
- Separate music, voice, and background sounds in an audio file
- Improve audio quality by removing noise or interference
- Find and summarize key segments in long audio recordings
Decision-Making
AI can:
- Recommend movies, music, or products in online stores and marketplaces
- Suggest words, outfits, photos, or interior design ideas
- Evaluate options and explain their reasoning
- Choose between several alternatives if asked to do so
Generating New Content
Neural networks can create entirely new content, including:
- Text: From poetry to research papers
- Images: Anything from cute cats to branding concepts or visualizations of complex ideas
- Music: Melodies, beats, or full songs in any genre using lyrics you provide
- Code: Programs, websites, and even full apps
Can You Trust Information Generated By AI?
Opinions on this topic vary. Below are some insights from recent research.
1
Public trust in AI responses is mixed.
A global study conducted with KPMG found that trust in AI is still a major challenge. Only 46% of people worldwide say they are willing to trust AI systems, even though many already use them regularly. The research surveyed more than 48000 respondents across 47 countries, highlighting the growing gap between widespread AI adoption and public confidence in these technologies.
2
People often trust AI answers even when they are not well supported.
Research shows that many users perceive AI responses as reliable and complete, even when they contain mistakes or misleading information. This effect appears even in sensitive areas such as healthcare. In some studies, participants trusted AI recommendations more than advice from human professionals.
3
It can be difficult to tell accurate information from incorrect AI responses.
Studies show that AI-generated content is often perceived as trustworthy. Many people believe they can easily recognize incorrect answers from AI systems. In reality, however, most users fail to notice mistakes in generated text.
4
Neural networks still make mistakes.
AI systems frequently produce errors, and many studies confirm this. For example, one large study found that in certain data-analysis tasks, models like GPT achieved accuracy rates between about 80% and nearly 97%. However, in more complex tasks, especially those involving recent news or precise dates, accuracy can drop to as low as 5%, while the rate of incorrect responses (often called AI hallucinations) can reach 91%.
Another study found that when answering questions about news events, about 45% of responses contained major errors, and 81% included at least some inaccuracies. In some cases, models invented events that never happened, confused dates or locations, or added details that were not present in the source information.
These errors are not random. They are a result of how AI systems work. Neural networks are trained on massive collections of text that contain both accurate information and conflicting data. When generating responses, they select statistically likely words and phrases rather than verifying facts. For this reason, even modern AI systems can't guarantee completely accurate answers, especially in areas that require precise factual information.
How To Start Using AI Tools From Scratch
Many people feel disappointed with AI after their first attempts to use them. In most cases, however, the problem isn't the model itself, it's how the request is written. The result largely depends on how clearly and specifically the user explains the task to the system. Once you learn a few simple principles, neural networks can start producing genuinely useful and high-quality results.
What a Prompt Is & Why It Matters
A prompt is the request, instruction, or task you give to a neural network. In simple terms, it's the way you tell the system what you want it to do.
A neural network cannot guess your intentions. It doesn't know your context, goals, or expectations unless you explain them. The prompt directly determines the quality of the response.
- A vague prompt → the system may generate a generic or irrelevant answer.
- A clear prompt → the response is much more useful, accurate, and tailored to your needs.
Let's look at a simple example:
Weak prompt: Write how to make money.
Stronger prompt: Suggest 10 ways a young person can make money remotely in the Netherlands. The person is just starting their career and is still exploring different interests.
Both prompts ask about earning money, but the second one will produce a far more useful result because it provides context and sets clear boundaries for the task.
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Step By Step: How To Get Better Results From AI
Step 1. Choose the right tool for your task
Different neural networks are designed for different purposes. Some work best with text, while others specialize in images, video, audio, or code (we'll cover this in more detail in the next chapter). At the beginning, you don't need to explore every tool available. Start with one that fits your task.
Keep in mind that different AI tools may respond differently to the same prompt and offer different capabilities. Trying two or three options can help you find the one that works best.
Step 2. Define your goal clearly
Before writing a prompt, decide what kind of result you want to receive. For example:
- Long text
- List of 20 practical ideas
- Sales plan
- Clear explanation of a topic
- Ready-to-use result such as a post, script, presentation, or website
Step 3. Write a clear prompt
Describe the task the same way you would explain it to a person.
Your prompt should include:
- Topic
- Key details of the task
- Desired tone or writing style
- Any personal preferences
- Response format
Example:
I'm preparing an educational presentation for my students. Explain in simple, easy-to-understand language which AI tools are suitable for school students. Write 3-5 paragraphs and include references to sources. The text should be written from a teacher's perspective, so adjust the tone accordingly.
I'm preparing an educational presentation for my students. Explain in simple, easy-to-understand language which AI tools are suitable for school students. Write 3-5 paragraphs and include references to sources. The text should be written from a teacher's perspective, so adjust the tone accordingly.
Step 4. Improve the result through follow-up prompts
Don't hesitate to refine the answer. You can ask the system to:
- Make it simpler
- Add an example
- Cut the text in half
- Rewrite it in a more formal style
- Add visuals or illustrations
Working with a neural network is a dialogue. The more clearly you express your requests, the better the results will become.
Step 5. Verify and edit the output
AI can make mistakes, oversimplify ideas, or drift away from the topic. Treat the generated result as a starting point: Check the facts, adapt the content to your needs, edit it to fit your specific task.
Which AI Tools Should You Choose?
The AI landscape today includes dozens of tools designed for different tasks: From simple writing assistants to powerful platforms for working with video, audio, and large datasets. Below are some of the most reliable and widely used options.
A well-known assistant for writing, explanations, brainstorming, and coding. The most common way to use this (and many other) AI tools is through a chat interface: You type a prompt and receive a response, or assign a task and get a completed result.
Offers both a free version and paid plans with more advanced capabilities.
An AI model designed for conversations, explanations, and programming tasks. It has quickly become popular among students in many countries.
A competitor to ChatGPT that performs particularly well in long conversations and detailed text analysis. It can also browse the Internet and open links for additional context.
Offers both a free version and a paid plan with expanded features.
An AI-powered search assistant that provides concise answers along with source links. Especially useful for fast and reliable information searches.
Offers both a free version and a paid plan with additional features.
A chat assistant with a strong focus on discussing current events and trending topics. It also includes tools for searching and generating different types of content.
Offers a free version with a limit on requests.
Best AI Tools For Image, Video, & Sound Generation
Allows users to generate images in many styles, from minimalistic designs to detailed 3D renders. Typically produces four variations so you can choose the best result.
Offers both a free version and a paid plan with expanded capabilities.
A platform for generating and editing videos using AI-powered tools.
Offers multiple pricing plans with different feature sets. New users can try 25 requests for free.
Creates songs and instrumental tracks. You can write your own lyrics or generate them with an AI assistant, choose a genre, and add instructions for style or performance.
Offers a free version with usage limits and paid plans with expanded functionality.
To make it more challenging, the prompt "no lyrics" was used.
A generative AI toolset for creating images, video, and audio with direct integration into Adobe's creative applications.
Offers both free and paid versions with additional features.
An advanced AI generator for images and visual concepts, widely used for artistic and conceptual visuals.
Available as a paid service.
Examples of AI images generated with Midjourney.
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