AI FUNDAMENTALS

Token

AI Fundamentals

Definition

A token is the basic unit of text processed by language models. It can correspond to a word, part of a word, or a punctuation character. For example, 'automation' might be a single token, while 'automatization' could be split into multiple tokens. The cost of AI services is often calculated based on the number of tokens processed.

Related terms

NLP & Language

Tokenization

Tokenization is the process of splitting a text into smaller units (tokens) that the AI model can process. Different models use different strategies: some split by words, others by subwords. Understanding tokenization helps estimate API costs (calculated per token) and optimize prompts to stay within the context window limits.

NLP & Language

Context Window

The context window is the maximum amount of text (measured in tokens) that an AI model can process in a single prompt. Models with larger context windows can analyze longer documents at once. Claude offers windows up to 200K tokens, allowing you to analyze entire technical manuals or complex contracts without splitting them.

AI Fundamentals

Language Model

A language model is an AI system trained on enormous amounts of text to understand and generate natural language. Large language models (LLMs) such as GPT and Claude can write texts, answer questions, translate, and reason about complex problems. For businesses, they represent versatile assistants for any text-based task.

EXPLORE

More terms in AI Fundamentals

Artificial Intelligence

Artificial intelligence is the computer science discipline that develops systems capable of performing tasks that normally require human intelligence: understanding language, recognizing images, making decisions. For Italian SMEs, AI represents a concrete opportunity to automate repetitive processes and achieve measurable competitive advantages.

Machine Learning

Machine learning is a subfield of AI in which systems automatically learn from data without being explicitly programmed for every scenario. ML algorithms analyze patterns in historical data to make predictions on new data. In business practice, it is used to forecast demand, classify documents, and optimize production processes.

Deep Learning

Deep learning is an advanced machine learning technique that uses neural networks with many layers (hence 'deep') to learn complex data representations. It underpins modern speech recognition, machine translation, and text generation systems. It requires large amounts of data but produces extraordinarily accurate results.

Neural Network

An artificial neural network is a computational model inspired by the workings of the human brain, composed of nodes (neurons) organized in interconnected layers. Each connection has a weight that is adjusted during training. Neural networks underpin nearly all modern AI systems, from image recognition to text generation.

Language Model

GPT

GPT (Generative Pre-trained Transformer) is the architecture behind ChatGPT, developed by OpenAI. It is a language model pre-trained on vast text corpora that can be used to generate content, answer questions, and perform complex text tasks. GPT-4 and subsequent versions have demonstrated increasingly advanced reasoning capabilities.

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