Token
The unit of text a language model reads and produces, typically a word or a fragment of a word. A model generates its answer one token at a time, each time choosing the next token from a probability distribution, which is the process that sampling settings such as temperature control.
Defined in 4 GAGE programs, which carry 6 distinct definitions of it. The wording above is taught in Certified AI Governance Professional (CAIGP).
How each discipline defines it
The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.
The unit of text a language model reads and produces, typically a word or a fragment of a word. A model generates its answer one token at a time, each time choosing the next token from a probability distribution, which is the process that sampling settings such as temperature control.
The basic unit of text an AI model processes, roughly three-quarters of an English word on average. Model APIs price per million tokens, and input and output tokens are priced differently (output is typically several times the input price).
A unit of text, often a whole word but frequently a word fragment, that an AI language model processes and generates; the basic unit both attention and generation operate on.
A fragment of text (often a word, part of a word, or a symbol) that a generative AI processes as a single unit; roughly 0.75 English words per token (OpenAI rule of thumb).
The basic unit of text an AI model processes, roughly three-quarters of an English word on average. Used to measure context window capacity and input or output length.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- Building Your AI Briefcase · Your AI Learning Companion, Certified AI Practitioner: Workplace Foundations
- What is AI? Definitions, History, and Types · AI Fundamentals, Certified AI Practitioner: Workplace Foundations
- How AI Actually Works: Algorithms, Data, and Training · AI Fundamentals, Certified AI Practitioner: Workplace Foundations
- Prompts, context, and why the same model gives different companies different answers · Build Before You Govern, Certified AI Governance Professional (CAIGP)
- How AI Actually Works Under the Hood: Architectures and Limits for Policy People · Technical Credibility Deep Dive, The AI Lobbyist: Certified AI Policy Strategist
- AI Investment Categories and Cost Structures · The Economics and the Business Case, Certified AI Transformation Professional (CATP)
Where it shows up at work
A definition is where Token starts. The lessons above teach it inside the competencies below, which are what job descriptions ask for and what the GAGE credential records. Every link here is drawn from the same careers map that builds a role's readiness check.
Explains training, tokens, context windows, embeddings, retrieval and fine-tuning well enough to ask an engineer a precise question and spot weak evidence.
3 lessons on this page teach it as a primary outcome.
Builds production systems on foundation models: retrieval augmented generation, structured tool use, evaluation harnesses, guardrails, and cost and latency budgets.
2 lessons on this page teach it as a primary outcome.
Builds an honest value model: baseline, measured change, cost, risk, and the projects that should be stopped.
One lesson on this page teach it as a primary outcome.
Roles that hire for it
Each role page shows the full competency set, what the seat decides, and a readiness check against your own evidence.
- AI Engineer, production systems editionneeds it at depth · 23 open postings this week
- Chief AI Officerneeds it at depth
- Chief Technology Officer, AI engineering editionneeds it at depth
- AI Security Architectneeds it at depth · 8 open postings this week
- AI Evaluation Specialistneeds it at depth · 6 open postings this week
- AI Governance Engineerneeds it at depth · 7 open postings this week
How job descriptions say it
Postings rarely use the textbook word. These are the phrases the careers map reads as asking for the competencies above.
Where the frameworks place it: NIST AI RMF.
One interview question about it
Explain how a large language model produces an answer, at the depth a governance decision needs and no deeper.
Asked of candidates for how models work, at a governance depth. Answering it in your own words is exactly how a GAGE topic is passed, and the credential records the pass where an employer can check it.
If you come from another field
Some backgrounds already carry this competency, and the readiness check gives credit for it.
- Data analysis, stewardship and business intelligence carries it as a claim to back up: SQL and analytical fluency make the technical layer approachable.
- Model risk, credit risk and quantitative analysis carries it as work you can show: Quantitative fluency is demonstrated in your work.
- A computer science or software engineering degree carries it as work you can show: You have trained or integrated a model in coursework or a project.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.