data provenance
Data provenance records where information came from, how it changed, and which people, systems, or processes handled it.
AI Terminology
Search clear AI terminology definitions, acronyms, related concepts and the reviewed videos that explain them.
Showing 61–80 of 1136 terms
Clear filtersexecution trace
An AI execution trace is a structured record of the steps, tool calls, state changes, and outputs produced during an AI workflow.
human judgment in AI
Human judgment in AI is the accountable interpretation and decision-making people contribute when setting goals, evaluating evidence, and managing consequences.
mixture-of-experts model
A mixture-of-experts model contains multiple specialized subnetworks and activates a selected subset for each input instead of using every parameter every time.
prompt caching
Prompt caching lets an AI service reuse previously processed prompt content so repeated context can cost less and run faster.
security monitoring
Security monitoring collects and analyzes system activity to detect suspicious behavior, control failures and emerging threats.
token volume
Token volume is the total number of input, output, cached, or reasoning tokens an AI workload processes over a defined period or task.
agent loop
An agent loop repeatedly interprets the current state, chooses an action, observes the result and decides what to do next.
agent tool use
Agent tool use lets an agent call bounded external capabilities so it can retrieve information or change state beyond generating a response.
agent workspace
An AI agent workspace is a shared environment where people and AI agents communicate, coordinate tasks, exchange files, and observe work state.
agentic commerce
Agentic commerce lets AI agents discover, recommend, and purchase goods or services within delegated financial controls.
AI-assisted code review
AI-assisted code review uses AI to inspect software changes for defects, risks, standards, missing tests, and opportunities for improvement while human reviewers retain responsibility.
model reliability
Model reliability is the degree to which an AI model produces dependable behavior across repeated, varied and operationally realistic use.
scheduled AI automation
Scheduled AI automation runs an AI-assisted workflow at defined times or recurring intervals without requiring a new manual start for every run.
token efficiency
AI token efficiency measures how effectively a model or workflow turns consumed input and output tokens into useful results.
training data
Training data is the collection of examples and signals used to adjust an AI model's parameters so it learns useful patterns.
chain-of-thought monitoring
Chain-of-thought monitoring analyzes a reasoning model's exposed intermediate reasoning for signs of errors, policy violations, deception, or unsafe plans.
evaluation set
An evaluation set is a collection of examples kept for measuring an AI system rather than training it.
game generation
Game generation uses AI to create or assemble playable assets, scenes, rules, audio and interactions.
inference throughput
AI inference throughput measures how much model-serving work a system completes per unit of time.