Large Language Model (LLM)
A type of AI trained on large amounts of text to generate and process natural language. Many of the prompts in your Briefcase are aimed at large language models, and their most relevant failure mode for governance is that they can produce fluent, confident output that is wrong, which is precisely why every Briefcase card names a failure mode and every artifact carries a human check.
Defined in 3 GAGE programs, which carry 5 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.
A type of AI trained on large amounts of text to generate and process natural language. Many of the prompts in your Briefcase are aimed at large language models, and their most relevant failure mode for governance is that they can produce fluent, confident output that is wrong, which is precisely why every Briefcase card names a failure mode and every artifact carries a human check.
A deep learning model trained on enormous quantities of text (and, increasingly, other data types for multimodal models) to predict the statistically most likely next piece of content, given everything that came before it in the prompt and generated response so far. LLMs power tools like ChatGPT, Claude, and Gemini.
A deep-learning model trained on vast text data that can generate, analyze, and transform text; examples include Claude, GPT, and Gemini.
An AI system trained to generate human-like text by predicting likely continuations. The support-bot failures anchoring this topic are LLM failures; the fabrication, variance, and silent-error modes are especially characteristic of LLMs.
An AI system that generates text by predicting the next words, given the text it has been shown. Because it reads instructions and data in the same channel and tends to follow the last convincing instruction, it is vulnerable to prompt injection by design, not by defect.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- What is AI? Definitions, History, and Types · AI Fundamentals, Certified AI Practitioner: Workplace Foundations
- Your command tools: the Briefcase, the professor, the dossier you will build · Taking Command, Certified AI Governance Professional (CAIGP)
- The license to govern: explaining to a skeptic exactly how your model fails · Build Before You Govern, Certified AI Governance Professional (CAIGP)
- Red-teaming your own system: attacks a motivated user will find · Evaluation and Trust, Certified AI Governance Professional (CAIGP)
- What AI Really Is: No Hype, No Myths, No Fear · Foundations of AI for Lobbyists, The AI Lobbyist: Certified AI Policy Strategist
Where it shows up at work
A definition is where Large Language Model (LLM) 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.
Recognizes hallucination, drift, skew, brittleness and biased outcomes, and knows how each one enters a system.
One lesson on this page teach it as a primary outcome.
Uses generative AI tools daily, knows what a model can and cannot do, and can say where an output should not be trusted.
One lesson on this page teach it as a primary outcome.
Explains training, tokens, context windows, embeddings, retrieval and fine-tuning well enough to ask an engineer a precise question and spot weak evidence.
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 Literacy Leadneeds it at depth
- AI Evaluation Specialistneeds it at depth · 6 open postings this week
- AI Model Validatorneeds it at depth
- Operations Manager, AI-readyneeds it at depth
- Model Risk Managerneeds it at depth
- AI Governance Analystneeds working knowledge · 3 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 MEASURE 2; ISO/IEC 42001; EU AI Act Article 4; DOL AI Literacy Framework.
One interview question about it
Tell me about a time a model was confidently wrong. How did you notice, and what did you change afterwards?
Asked of candidates for model failure modes and bias recognition. 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.
- A data science, statistics or analytics degree carries it as work you can show: Bias and failure modes were in your projects, not only your reading.
- Recent graduate in law, policy, business or data carries it as a claim to back up: Most graduates already use the tools daily.
- Teachers, trainers and instructional designers carries it as a claim to back up: Most educators are already using AI in preparation.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.