Glossary
Every bit of jargon, in one sentence.
- benchmark
- A standard test used to compare how well different AI models perform on the same task.
- embedding
- A way of turning words, images, or papers into lists of numbers so a computer can measure how similar they are.
- fine-tuning
- Taking an already-trained model and training it a bit more on a specific task or style.
- inference
- Actually using a trained model to make a prediction or generate an answer (as opposed to training it).
- neural network
- A model loosely inspired by the brain, made of layers of simple units that pass numbers to each other to learn patterns.
- parameter
- One of the adjustable numbers inside a model. Big models have billions of them.
- training
- The process of showing a model many examples and nudging its internal numbers until it gets good at a task.
- transformer
- A neural-network design that learns which parts of the input to pay attention to. It powers most modern language models.