Terms
Every term in this hub.
This hub collects the foundational terms in the Prefactor glossary. The models group covers the substrate: LLMs, foundation models, and generative AI, defined precisely enough to be useful rather than expansively enough to be marketing. If the difference between a model and an agent is still fuzzy, the agent concepts group is the fix: it covers what an agent is, what tasks and system prompts are, and the emergent behaviours that make agents more than the sum of their prompts.
The learning paradigms group covers how models acquire capability: pre-training, few-shot and zero-shot learning. These entries matter for a practical reason: how a capability was acquired predicts how reliably it performs, and several evaluation practices in the evaluation hub only make sense against that background.
The standards group covers the reference points the industry is converging on: ISO 42001, the OWASP LLM Top 10, MITRE ATLAS, and the strategy and role vocabulary that appears once organisations take AI adoption seriously. These are subject-matter definitions of external frameworks, kept because teams keep meeting them in procurement and audits.
An honest framing for this hub: it is a reference shelf, not a reading path. Nobody studies fundamentals front to back; you arrive here from a specific term in a specific context. The groups below are organised for that kind of visit, and every entry links onward to the specialised hub where its practical life happens.
Models
Foundation Model A foundation model is a large AI model trained on broad data that can be adapted to many downstream tasks through fine-tuning, prompting, or tool integration. Generative AI Generative AI refers to AI systems that create new content, text, images, code, audio, or video, rather than simply classifying or predicting from existing data. Large Language Model (LLM) A large language model is a neural network trained on vast amounts of text data that can generate, summarise, translate, and reason about natural language. llms.txt llms.txt is a proposed convention: a markdown file served at the root of a site that gives language models a curated guide to its most useful content. Model Factsheet A model factsheet is a structured document that describes a model's intended use, training methodology, performance characteristics, known limitations, and ethical considerations. Model Risk Management Model risk management is the discipline of identifying, measuring, and mitigating the risks that arise from using quantitative models, including AI models, for business decisions. OWASP Top 10 for LLM Applications The OWASP Top 10 for Large Language Model Applications is a community-developed list of the most critical security risks in LLM-based systems, including prompt injection, insecure output handling, training data poisoning, and excessive agency. World Model A world model is an internal representation an AI agent builds of its environment, including the state of systems, the consequences of actions, and the expectations of users.
Learning paradigms
Few-Shot Learning Few-shot learning is a prompting technique where a small number of examples are included in the prompt to guide the model's behavior on a specific task. Training Data Poisoning Training data poisoning is an attack where an adversary corrupts some of the data used to train or fine-tune an AI model, causing the model to develop specific biases, backdoors, or vulnerabilities. Zero-Shot Learning Zero-shot learning is when a model performs a task it was not explicitly trained or given examples for, relying solely on its pre-trained knowledge and the task instruction.
Agent concepts
Agent Owner An agent owner is the person or team accountable for an AI agent's behavior, compliance, and operational health throughout its lifecycle. Agentic Coding Agentic coding is software development where an AI agent plans and carries out multi-step coding work: running tools, editing files, and testing its changes. Context Consumer A context consumer is any agent, model, or application that reads context from another system so it can make decisions or complete a task. Emergent Behavior Emergent behavior is capability or conduct that appears in an AI system without being explicitly programmed or trained for. Event Sourcing (Agent) Event sourcing for AI agents stores every state change and action as an immutable, append-only sequence of events, rather than only the current state. Indirect Prompt Injection Indirect prompt injection is an attack where malicious instructions are embedded in data the agent retrieves, such as documents, emails, or web pages, rather than in the user's direct input. MiFID II (AI Agents) MiFID II is the EU regulatory framework for financial markets that imposes requirements on automated decision-making in investment services. Semantic Context Semantic context is context enriched with meaning, relationships, or labels rather than raw values alone. System Prompt A system prompt is the set of instructions given to a language model that defines the agent's role, behavior, constraints, and personality. Task Agent A task agent is designed to complete a specific, well-defined job, such as processing an invoice, triaging a support ticket, or generating a report, then return the result.
Standards & frameworks
Strategy & roles
AI Center of Excellence An AI center of excellence is a cross-functional team within an organisation that establishes standards, best practices, and governance frameworks for AI adoption. AI Maturity AI maturity describes how advanced an organisation is in its AI adoption, from experimenting with individual use cases to running governed, production-grade agents at scale. AI Risk Officer An AI risk officer is responsible for identifying, assessing, and managing the risks associated with AI systems across the organisation. AI Singularity The AI singularity is a theoretical point at which AI systems become capable of recursive self-improvement, leading to rapid and potentially uncontrollable advances in intelligence. AI Strategy An AI strategy is an organisation's plan for adopting, scaling, and governing AI to achieve business objectives. Responsible AI Officer A responsible AI officer is a senior role accountable for an organisation's AI ethics, governance, and compliance strategy. Value Alignment Value alignment is the challenge of ensuring an AI agent's actions are consistent with the values and preferences of the humans it is meant to serve, not just technically correct but substantively beneficial.
More ai agent fundamentals terms
AI Attribution AI attribution is the practice of disclosing when content, decisions, or actions were generated or influenced by an AI system. AI Audit An AI audit is a formal review of an AI system's design, data, behavior, and governance controls to verify compliance with internal policies, regulations, or industry standards. AI Bill of Materials (AI BOM) An AI bill of materials is a comprehensive inventory of all components that make up an AI system, including models, training data sources, frameworks, libraries, tools, APIs, and configuration. Artificial General Intelligence (AGI) Artificial general intelligence refers to a hypothetical AI system capable of understanding and performing any intellectual task a human can. Capability Discovery Capability discovery is the process of learning what another agent or service can do before calling it. Content Filtering Content filtering is the process of screening AI-generated text, images, or other outputs to block or flag content that violates policies, such as harmful, misleading, offensive, or non-compliant material. Corrigibility Corrigibility is the property of an AI agent that makes it responsive to correction, shutdown, and modification by its operators, without resisting, circumventing, or manipulating humans in order to preserve its current goals. Data Lineage Data lineage tracks the origin, transformations, and movement of data through systems. Explainability Explainability is the ability to provide a human-understandable account of why an AI system produced a particular output or decision. Spec-Driven Development Spec-driven development is a practice where a written specification, not a prompt, is the source of truth an AI coding agent implements from. Supply Chain Risk (AI) AI supply chain risk arises from dependencies on external models, data sources, APIs, tools, and libraries that are outside the organisation's direct control. Third-Party AI Risk Third-party AI risk is the exposure an organisation faces from AI models, agents, or services provided by external vendors. Watermarking (AI) AI watermarking is the practice of embedding imperceptible signals into AI-generated content to indicate its origin.
Questions
Common questions.
What is the difference between an LLM and an AI agent?
An LLM is a model that generates text; an agent is a system that uses a model as its reasoning engine to plan, call tools, and complete tasks. The agent is the loop and the plumbing, not just the model inside it.
Which standards apply to AI agents?
The ones teams meet most are ISO 42001 for AI management systems, the OWASP LLM Top 10 for risks, and MITRE ATLAS for adversarial techniques. Each has a subject-matter entry in this hub.