Model
ELI5 — The Vibe Check
A model is the trained AI — the finished product. It's a massive file (or set of files) full of numbers (weights) that, when you run text through it, produces useful output. When you call an API and specify 'gpt-4' or 'claude-opus-4-6,' you're choosing which model to use. Each model has different capabilities and costs.
Real Talk
In machine learning, a model is the parameterized mathematical function learned from data. For neural networks, the model is defined by its architecture and the learned weight values. 'Model' can refer to the architecture blueprint, the trained weights, or both together. Models are versioned, benchmarked, and deployed as services.
When You'll Hear This
"Which model are you using?" / "The model outputs JSON when given this prompt."
Related Terms
Fine-tuning
Fine-tuning is like taking a smart graduate student who knows everything and then sending them to a specialist bootcamp.
Inference
Inference is when the AI actually runs and generates output — as opposed to training, which is when it's learning.
LLM (Large Language Model)
An LLM is a humongous AI that read basically the entire internet and learned to predict what words come next, really really well.
Parameters
Parameters is the technical word for weights — the individual numbers inside an AI model. When someone says 'GPT-4 has 1.
Training
Training is the long, expensive process where an AI learns from data.
Weights
Weights are the numbers inside a neural network that determine what it knows and how it behaves — they're the AI's 'brain cells.