How AI Models Work
AI models are probabilistic — understand deterministic vs probabilistic systems, model choice, and modalities.
Let us talk about how AI models work.
You can think of them like very smart, general-purpose API endpoints. As with an API request, you send input and receive output.
The biggest difference is this: you will not always get the same result every time.
Deterministic vs probabilistic
Traditional software is deterministic. Given the same input, if you run the same program, you get the same output every time.
AI models are different. They are probabilistic. That means there are many possible paths a model can take, and the path can change each time.
The first part of your AI mental model is: never assume you will definitely get the same answer every time.
When an AI model chooses a path, how does it decide what to produce? Behind the scenes, the model uses two main sources of information:
- Information the model was trained on.
- Information you provide in the current conversation.
The first source is fixed when the model is trained. The second source is the one you control.
The model matters
Different models have different strengths. Some are faster and cheaper. Some are better at reasoning. Some are better at writing, coding, or processing images.
When you choose a model, you are choosing a tradeoff between cost, speed, quality, and capability.
For simple autocomplete, a fast model may be enough. For architectural planning, debugging a hard issue, or reviewing a subtle code change, you usually want a stronger model.
Modalities
Models can work with different kinds of input and output. Text is the most common, but modern models can also understand images, audio, and structured data depending on the tool.
For programming work, this matters because your context is not only prose. It can include code, terminal output, screenshots, logs, diagrams, API responses, and design mockups.
The better you are at giving the model the right input, the more useful the output becomes.
What to remember
AI models are not magic and they are not deterministic programs. They are probabilistic systems that generate likely outputs from training and context.
Your job is to provide the right context, choose the right model for the task, and verify the result.