Core Concepts of AI: Beyond ChatGPT | Agentic AI Series

Everyone’s talking about AI, but most people only see the surface. Tools like ChatGPT have made AI feel like magic, but if you’re serious about the core concepts of AI and using AI in real-world projects, you need to understand what’s happening under the hood. This post is here to make the fundamentals clear and practical, so you can build and work with AI confidently, not just as a user, but as a creator and decision-maker. This article is part of the Agentic AI series, where we go step by step from the basics to actually building AI agents. If you stick with the series, you’ll go from theory to hands-on experience with the kinds of AI that are reshaping software today.

What Are the Core Concepts of AI?

When you get past the hype, AI is just a set of tools and ideas that help computers learn from examples, find patterns in data and make decisions. Understanding the core concepts of AI is what allows you to see through the buzzwords and focus on what actually works.

At its core, AI is about three big ideas:

1. Data
2. Algorithms
3. Models

Let’s walk through each, using plain English and practical examples.

Data: The Raw Material for AI

No data, no AI.
Everything starts with data. Think about the photos on your phone, the words in your emails, the sales numbers in your company, or the sensor readings from a smart device. All of these are types of data and each can be used to train an AI system.

But not all data is useful. What matters is having enough examples and making sure your data is accurate and relevant. If your data is messy or biased, your AI will learn the wrong things.

A few things to keep in mind:

• Start with data that represents the real-world problem you want to solve.
• Clean your data so it’s free of errors and inconsistencies.
• Make sure your data is labeled if you’re planning to use supervised learning (more on this in a minute).

Algorithms: The Rules That Help AI Learn

If data is the raw material, algorithms are the instructions for making sense of it. An algorithm in AI is just a step-by-step way to find patterns or make predictions based on your data.

You’ll hear about all kinds of algorithms in AI, but a few are especially common:

• Classification algorithms: These help AI sort things into categories. For example, is this email spam or not?
• Regression algorithms: These predict numbers, like tomorrow’s temperature or the price of a house.
• Clustering algorithms: These group data points that are similar to each other, often used for customer segmentation.

The key thing to remember is this: You don’t need to invent your own algorithms. Most of the time, you’ll use algorithms that have been tried and tested. What matters is picking the right one for your problem.

Models: The Brains of the Operation

A model is what you get when you run your data through an algorithm. Think of it as the “trained” AI. It’s what makes predictions or classifications on new data, even if it’s never seen that data before.

For example, after training a model on thousands of labeled cat and dog photos, the model can look at a new photo and tell you if it’s a cat or a dog. The better your data and the more suitable your algorithm, the more accurate your model will be.

Here’s the typical cycle:

• Collect and clean your data.
• Pick the right algorithm.
• Train your model.
• Test how well the model works on new data.
• Keep improving as you get more data or see new situations.

How AI Learns: Training and Inference

There are two main stages when working with AI models: training and inference.

• Training is where you show the model lots of examples and it learns the patterns. This part usually takes the most computing power and time.
• Inference is when you use the trained model to make predictions on new data, often instantly and at scale.

Imagine training is like studying for an exam and inference is the moment you answer the test questions.

Types of Learning in AI

Not every AI learns in the same way. Here are the big three:

Supervised learning:
The most common. You give the model labeled data (where you know the right answer). The model learns by example. For instance, showing a model thousands of labeled emails, some marked “spam,” some “not spam.”

Unsupervised learning:
No labels this time. The model tries to find structure or groupings in the data on its own. Think about customer segmentation or finding outliers in sales data.

Reinforcement learning:
Here, the AI learns by trial and error, getting feedback based on the results. This is how game-playing AIs and some robotics systems improve over time.

Each of these opens up different possibilities and knowing which to use is a big part of building with AI.

Neural Networks and Deep Learning: Why Modern AI Is So Powerful

You’ve probably heard about neural networks and deep learning.
These are the techniques behind much of the recent excitement in AI, including systems like ChatGPT, image recognition, speech-to-text and even self-driving cars.

A neural network is built from layers of tiny decision-makers called “neurons,” loosely inspired by the brain. When you stack many layers together, you get deep learning. This approach is great for spotting complex patterns, like recognizing faces in photos or understanding language.

• Convolutional Neural Networks (CNNs): Used mostly for images and video.
• Recurrent Neural Networks (RNNs): Great for sequences, like text or time-series data.

These tools let you tackle problems that traditional algorithms just couldn’t handle.

Evaluating AI: Accuracy, Bias and Trust

No AI is perfect. After you build a model, you need to check how well it works.
That means looking at:

• Accuracy: How often does the model get it right?
• Precision and recall: Does it miss important things, or is it too eager and makes mistakes?
• Bias: Does the model treat all data fairly, or does it make wrong assumptions based on flawed data?

Testing and monitoring your AI is just as important as building it. And if the data changes over time, or your users change, your model may need updating.

From Core Concepts to Agentic AI

Understanding these basics is what lets you move beyond using AI as a black box.
The next frontier is agentic AI—systems that don’t just process data, but act on it, make decisions and sometimes work together to solve real problems.

If you know the core concepts of AI, you’re ready to dive into building AI agents that automate workflows, interact with users, or even collaborate with other systems. That’s what we’ll explore in the upcoming posts in the Agentic AI series.

Keep Going: What’s Next in the Agentic AI Series

This is just the foundation. If you want to see how these concepts come together in real projects, follow along in the series.
Up next:

• Demystifying what an AI agent really is
• How agentic AI changes what’s possible in software
• Building your first AI agent from scratch

Bookmark this page, leave your questions in the comments, or share your own experience working with AI. Real progress starts with a solid foundation and now you’ve got it.