Prompt Template

The field of AI that lets computers read, interpret, translate and generate human language, from spam filters and search to chatbots.

The unit of text a language model reads and writes: a word, part of a word, or a character, drawn from the model's fixed vocabulary.

Splitting text into tokens, usually subword pieces, and mapping each to an integer ID so a language model can process it.

Assigning probabilities to sequences of text, in practice by predicting each next token from the ones before it; the objective behind large language models.

A collection of data examples used for training, validating, or testing machine learning models.

The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.

Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.

Learning from unlabeled data to discover hidden patterns, structures, or relationships without explicit target outputs.

Training on unlabeled data by making the data supply its own answers: hide or alter part of an input and have the model predict or match it.

Training a model on a large dataset (often self-supervised) before fine-tuning on specific tasks, enabling transfer learning.

A single measurable property of an example, such as a house's floor area or how many links an email contains, used as an input to a model.

A model built from layers of simple units, each taking a weighted sum of its inputs and applying a nonlinearity; training adjusts the weights so the whole stack maps inputs to useful outputs.

A subset of machine learning that uses neural networks with multiple layers (deep neural networks) to learn hierarchical representations of data.

Letting a model learn its own internal description of the data, such as embeddings or layered visual features, instead of relying on features designed by hand.

A list of numbers (a vector) that represents a word, sentence, image or other item, learned so that similar items end up close together.

A technique that lets a neural network weigh every part of its input when producing each output, focusing on the parts most relevant at that step.

A neural network architecture, introduced in 2017, built from stacked self-attention and feed-forward layers; the basis of nearly every modern large language model.

A neural network, almost always a transformer, trained on vast amounts of text to predict the next token, which lets it write, answer, summarize and follow instructions.

Designing a model's input (instructions, context, examples and output format) to get better results without changing the model itself.