When you use ChatGPT or Google's AI, have you wondered: how did someone actually make this? Here's the plain-English version of how AI gets built.

Step 1: Decide what problem to solve

Every AI starts with a problem. "Can we make a computer that recognises spam emails?" "Can we build a tool that translates between languages?" "Can we create an AI that helps kids learn to read?" The problem defines everything that comes after.

Step 2: Collect training data

AI learns from examples — and it needs a LOT of them. This collection of examples is called training data.

  • To build a spam filter, you need thousands of labelled emails: "spam" or "not spam"
  • To build an image classifier, you need thousands of labelled photos
  • To build ChatGPT, OpenAI used billions of pages of text from the internet, books, and websites

The quality and diversity of training data is crucial. If the data is biased or incomplete, the AI will be too.

Step 3: Choose an AI model architecture

This is the "shape" of the AI — how it's structured. AI researchers design different structures for different problems. The most powerful modern AIs use a structure called a neural network, loosely inspired by how neurons in the human brain connect to each other. For language AI like ChatGPT, a specific type called a transformer is used.

Step 4: Training — the AI learns from data

This is where the magic (and the computing power) happens. During training:

  1. The model looks at an example from the training data
  2. It makes a prediction (e.g., "Is this email spam? I say... no.")
  3. It checks if it was right
  4. It adjusts itself slightly based on the error
  5. It repeats this millions or billions of times

For large AI models like ChatGPT, training takes weeks or months on thousands of specialist computer chips (called GPUs) — and costs millions of pounds.

Step 5: Evaluation — testing how well it learned

After training, researchers test the model on new examples it has never seen. This checks whether it actually learned the pattern or just memorised the training examples. A good AI generalises — it can handle new situations it wasn't trained on.

Step 6: Fine-tuning and safety checks

For AI assistants like ChatGPT, there's an extra step: fine-tuning with human feedback. Human trainers rate the AI's responses and teach it to be more helpful, accurate, and safe. This is why ChatGPT refuses certain harmful requests — it was trained by humans to recognise and decline them.

Step 7: Deployment — making it available

Once the AI works well enough, it's deployed — made available to users. This is when you start using it. Even after deployment, the AI can continue to improve based on new data and user feedback.

Why AI can be wrong or biased

If the training data contains mistakes, the AI learns mistakes. If the training data underrepresents certain groups of people, the AI may perform worse for those groups. This is called AI bias — and it's a real problem that AI researchers work hard to address. Understanding this helps you use AI critically rather than trusting it blindly.

Can kids build their own AI?

Yes — at a beginner level! Tools like Google Teachable Machine let kids train a simple image or sound classifier in minutes, right in a browser, with no coding required. You can train it to recognise your hand gestures, your face vs a sibling's face, or different sounds. It's the same basic process as above, just much simpler. The AI Adventures course includes hands-on AI building projects for kids aged 9-14.