What is an AI model and how is it different from an algorithm?

An AI model is essentially a program that has learned patterns from data so it can make predictions or decisions, while an algorithm is just the set of rules it follows to do that learning. Think of it like this: a recipe for chocolate chip cookies is the algorithm. It's the step-by-step instructions. The AI model is the batch of cookies you actually pull out of the oven — the finished product shaped by the specific ingredients and your oven's quirks. The algorithm tells the system *how* to learn, and the model is *what* it learned. A linear regression algorithm, for example, always uses the same math to find a line of best fit. But if you run it on housing data, the resulting model predicts house prices. Run the same algorithm on sales data, and the model predicts quarterly revenue. Same recipe, completely different cookies. This distinction matters because when people say they're 'training an AI,' they're feeding data to an algorithm so it can build a model. The model's quality depends entirely on the data it saw. Feed a cookie recipe bad flour, and you'll taste it. I've seen companies get frustrated when their AI model performs poorly, not realizing the algorithm was fine — the training data was just messy or biased. According to Google's 2025 AI Principles documentation, model behavior is a direct reflection of the training data and objectives set by developers, not a flaw in the underlying math. For a deeper dive into how these models generate text, see our guide on how to write AI prompts. **Related**: How does an AI model actually 'learn' from data? | What's the difference between training and inference in AI?