AI Glossary
Learn the language of Artificial Intelligence with The Tool Money Lab's growing library of clear, jargon-free explanations — written for founders, operators and curious professionals.
AI Agent
An AI agent is a language-model-powered system that plans and executes multi-step tasks by choosing which tools or APIs to call in order to reach a user-defined goal.
AI Hallucination
A hallucination is an LLM output that is fluent and plausible but factually incorrect or unsupported by any reliable source.
Context Window
The context window is the token budget an LLM can read and write within a single request, covering the prompt, retrieved data, previous turns and the response itself.
LLM
An LLM is a neural network trained on very large text datasets to predict the next token in a sequence, which enables general-purpose language understanding and generation.
MCP (Model Context Protocol)
The Model Context Protocol is an open standard for connecting AI models to external tools, data and services through a shared JSON-RPC interface.
Prompt Engineering
Prompt engineering is the discipline of designing inputs to a language model so its outputs are accurate, useful and repeatable.
Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation is a pattern in which relevant documents are retrieved from an external source and injected into an LLM's prompt so the model can answer with grounded, up-to-date, private information.
AI Agent
An AI agent is a language-model-powered system that plans and executes multi-step tasks by choosing which tools or APIs to call in order to reach a user-defined goal.
LLM
An LLM is a neural network trained on very large text datasets to predict the next token in a sequence, which enables general-purpose language understanding and generation.
Prompt Engineering
Prompt engineering is the discipline of designing inputs to a language model so its outputs are accurate, useful and repeatable.
Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation is a pattern in which relevant documents are retrieved from an external source and injected into an LLM's prompt so the model can answer with grounded, up-to-date, private information.
AI Agent
An AI agent is a language-model-powered system that plans and executes multi-step tasks by choosing which tools or APIs to call in order to reach a user-defined goal.
LLM
An LLM is a neural network trained on very large text datasets to predict the next token in a sequence, which enables general-purpose language understanding and generation.
Prompt Engineering
Prompt engineering is the discipline of designing inputs to a language model so its outputs are accurate, useful and repeatable.
Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation is a pattern in which relevant documents are retrieved from an external source and injected into an LLM's prompt so the model can answer with grounded, up-to-date, private information.
Context Window
The context window is the token budget an LLM can read and write within a single request, covering the prompt, retrieved data, previous turns and the response itself.
AI Tokens
A token is the unit of text an LLM processes — typically 3-4 characters or roughly three-quarters of an English word. Prompts, replies and pricing are all measured in tokens.
AI Agent
An AI agent is a language-model-powered system that plans and executes multi-step tasks by choosing which tools or APIs to call in order to reach a user-defined goal.
LLM
An LLM is a neural network trained on very large text datasets to predict the next token in a sequence, which enables general-purpose language understanding and generation.
Prompt Engineering
Prompt engineering is the discipline of designing inputs to a language model so its outputs are accurate, useful and repeatable.
Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation is a pattern in which relevant documents are retrieved from an external source and injected into an LLM's prompt so the model can answer with grounded, up-to-date, private information.
Context Window
The context window is the token budget an LLM can read and write within a single request, covering the prompt, retrieved data, previous turns and the response itself.
AI Tokens
A token is the unit of text an LLM processes — typically 3-4 characters or roughly three-quarters of an English word. Prompts, replies and pricing are all measured in tokens.
AI Agent
An AI agent is a language-model-powered system that plans and executes multi-step tasks by choosing which tools or APIs to call in order to reach a user-defined goal.
AI Automation
AI automation is the use of AI models to perform tasks — usually language, vision or decision tasks — that were previously done manually because they required judgement.
AI Hallucination
A hallucination is an LLM output that is fluent and plausible but factually incorrect or unsupported by any reliable source.
AI Tokens
A token is the unit of text an LLM processes — typically 3-4 characters or roughly three-quarters of an English word. Prompts, replies and pricing are all measured in tokens.
AI Workflow
An AI workflow is an orchestrated sequence of steps in which one or more AI models cooperate with tools, APIs and human approvals to complete a task.
API
An API (Application Programming Interface) is a defined set of endpoints and rules that lets one program request functionality or data from another.
Context Window
The context window is the token budget an LLM can read and write within a single request, covering the prompt, retrieved data, previous turns and the response itself.
Few-shot Prompting
Few-shot prompting is a prompting technique that includes a small number of input/output examples in the prompt to steer the model toward the desired format or behaviour.
Agentic AI
Agentic AI refers to systems where a language model autonomously decomposes goals, selects tools and executes multi-step actions with minimal human intervention.
Chain-of-Thought Prompting
Chain-of-thought (CoT) prompting is a technique where the model is asked to produce intermediate reasoning steps before its final answer, improving performance on multi-step problems.
Embeddings
An embedding is a dense vector produced by a model that maps content into a space where distance corresponds to semantic similarity.
Fine-tuning
Fine-tuning is additional training applied to a pretrained model on curated examples so the model specialises in a task, tone, format or domain.
MCP (Model Context Protocol)
The Model Context Protocol is an open standard for connecting AI models to external tools, data and services through a shared JSON-RPC interface.
Reinforcement Learning
Reinforcement learning is a machine learning paradigm in which an agent learns to make decisions by receiving reward signals for its actions in an environment.
Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation is a pattern in which relevant documents are retrieved from an external source and injected into an LLM's prompt so the model can answer with grounded, up-to-date, private information.
Transformer Model
A transformer is a neural network architecture built around self-attention, which enables efficient modelling of long-range dependencies in sequences.
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