Define the new internet.
Look up the words people use online, add the ones we missed, and help make the internet easier to understand.
Look up the words people use online, add the ones we missed, and help make the internet easier to understand.
2,337 definitions
Model Instruction Boundary is a ai policy boundary that separates durable system instructions from user-provided content for foundation model behavior and serving. It uses role labels, precedence rules, and prompt assembly checks so teams can avoid instruction confusion while keeping evidence, reliability, and public-safe operational boundaries clear.
“The AI platform team used Model Instruction Boundary when the model produced a low-confidence answer, so the team could avoid instruction confusion before the agent workflow reached production.”
Context Citation Builder is a ai attribution helper that formats source links and evidence for an AI answer for runtime memory and retrieved information. It uses canonical URLs, source titles, and quote limits so teams can make generated answers citeable while keeping evidence, reliability, and public-safe operational boundaries clear.
“The AI platform team used Context Citation Builder when the context window filled with mixed sources, so the team could make generated answers citeable before the agent workflow reached production.”
Context Human Approval is a ai control step that requires a person to approve sensitive or high-impact actions for runtime memory and retrieved information. It uses risk scoring, review UI, and audit logs so teams can keep protected decisions accountable while keeping evidence, reliability, and public-safe operational boundaries clear.
“The AI platform team used Context Human Approval when the context window filled with mixed sources, so the team could keep protected decisions accountable before the agent workflow reached production.”
Context Response Schema is a ai output contract that requires model output to match a known structure for runtime memory and retrieved information. It uses JSON schemas, validators, retries, and error reporting so teams can make responses machine-readable while keeping evidence, reliability, and public-safe operational boundaries clear.
“The AI platform team used Context Response Schema when the context window filled with mixed sources, so the team could make responses machine-readable before the agent workflow reached production.”
Context Fallback Path is a ai resilience pattern that keeps an AI feature useful when a provider or tool is unavailable for runtime memory and retrieved information. It uses degraded states, deterministic responses, and operator notices so teams can avoid fake AI success while keeping evidence, reliability, and public-safe operational boundaries clear.
“The AI platform team used Context Fallback Path when the context window filled with mixed sources, so the team could avoid fake AI success before the agent workflow reached production.”
Context Agent Trace is a ai observability record that captures the steps an AI workflow took for runtime memory and retrieved information. It uses trace identifiers, tool events, and redacted metadata so teams can debug agent behavior without exposing secrets while keeping evidence, reliability, and public-safe operational boundaries clear.
“The AI platform team used Context Agent Trace when the context window filled with mixed sources, so the team could debug agent behavior without exposing secrets before the agent workflow reached production.”
Context Memory Scope is a ai state boundary that limits what an assistant may remember or reuse for runtime memory and retrieved information. It uses retention policies, consent checks, and namespace separation so teams can prevent accidental cross-context leakage while keeping evidence, reliability, and public-safe operational boundaries clear.
“The AI platform team used Context Memory Scope when the context window filled with mixed sources, so the team could prevent accidental cross-context leakage before the agent workflow reached production.”
Context Safety Filter is a ai policy control that detects content that should be blocked, rewritten, or escalated for runtime memory and retrieved information. It uses classifiers, rules, and human review queues so teams can keep outputs public-safe while keeping evidence, reliability, and public-safe operational boundaries clear.
“The AI platform team used Context Safety Filter when the context window filled with mixed sources, so the team could keep outputs public-safe before the agent workflow reached production.”
Context Grounding Check is a ai quality control that verifies that generated answers are backed by available sources for runtime memory and retrieved information. It uses citation checks, retrieval evidence, and contradiction detection so teams can reduce unsupported claims while keeping evidence, reliability, and public-safe operational boundaries clear.
“The AI platform team used Context Grounding Check when the context window filled with mixed sources, so the team could reduce unsupported claims before the agent workflow reached production.”
Context Model Router is a ai selection service that chooses the best model or provider for a task for runtime memory and retrieved information. It uses cost, latency, capability, policy, and fallback signals so teams can match work to the right model while keeping evidence, reliability, and public-safe operational boundaries clear.
“The AI platform team used Context Model Router when the context window filled with mixed sources, so the team could match work to the right model before the agent workflow reached production.”