A reference from Weave

Find a term

Understand the metrics, models, and methods behind modern engineering. Clear definitions, practical examples, and a closer look at what the numbers actually mean.

Terms beginning with I

87 terms

  • Idea to production time

    Idea to production time is a software delivery concept used to describe a specific event, interval, control, or operating condition in the path from source change to production behavior. A useful definition names the boundary, unit, and decision the measure supports.

    DORA and DevOps

  • Idempotency key

    Idempotency key is a model-routing or gateway concept used to manage reliable request governance for AI requests. It describes a distinct decision, control, interface, or observation point between an application and one or more model providers.

    Model routing and gateways

  • Identity resolution

    Identity resolution matches the same person, team, service, or item across sources.

    Engineering analytics

  • Idle capacity

    Idle capacity is unused capacity under a stated boundary and time window. It can be valuable slack that absorbs variation, or it can signal missing demand, a dependency, or a policy that prevents safe pulling.

    Flow and capacity planning

  • Immutable artifact

    Immutable artifact is a release engineering and DevOps concept for controlling how software changes are prepared, introduced, or understood.

    DORA and DevOps

  • Immutable infrastructure

    Immutable infrastructure is a software delivery concept used to describe a specific event, interval, control, or operating condition in the path from source change to production behavior. A useful definition names the boundary, unit, and decision the measure supports.

    DORA and DevOps

  • Impact effort matrix

    Impact effort matrix is a concept used in software delivery planning to describe a condition, relationship, estimate, or decision about engineering work.

    Flow and capacity planning

  • Importance sampling

    Importance sampling is a defined lens for examining AI system behavior with a stated task, evidence, and interpretation rule.

    Evaluations and benchmarks

  • Improvement hypothesis

    Improvement hypothesis is a way to organize, support, or evaluate software work so that teams can make useful progress with less avoidable friction. It is most valuable when connected to a concrete outcome and the local conditions of the team using it.

    Developer productivity

  • Imputation

    Imputation is a statistical or measurement concept used to describe, compare, or interpret engineering data. Its meaning depends on the unit of analysis, data-generating process, and question being asked.

    Measurement and experimentation

  • In-context learning

    In-context learning is a language-model concept about context selection and limits. It names a mechanism, representation, prompting pattern, decoding control, or context behavior that can change how an AI system processes input and produces output.

    LLM fundamentals

  • Inappropriate intimacy

    A code smell in which two modules know too much about each other’s internal details.

    Code quality and technical debt

  • Incident alert

    Incident alert is a notification that evidence suggests a disruption, degradation, or risk requiring coordinated attention.

    Reliability and observability

  • Incident bridge

    Incident bridge is a reliability concept used to describe a specific condition, control, or decision in the operation of software services.

    Reliability and observability

  • Incident budget

    Incident budget is a software delivery concept used to describe a specific event, interval, control, or operating condition in the path from source change to production behavior. A useful definition names the boundary, unit, and decision the measure supports.

    DORA and DevOps

  • Incident channel

    Incident channel is a reliability concept used to describe a specific condition, control, or decision in the operation of software services.

    Reliability and observability

  • Incident closure

    Incident closure is a reliability concept used to describe a specific condition, control, or decision in the operation of software services.

    Reliability and observability

  • Incident commander handoff

    Incident commander handoff is a reliability concept used to describe a specific condition, control, or decision in the operation of software services.

    Reliability and observability

  • Incident communications plan

    Incident communications plan is a reliability concept used to describe a specific condition, control, or decision in the operation of software services.

    Reliability and observability

  • Incident detection latency

    Incident detection latency is a reliability concept used to describe a specific condition, control, or decision in the operation of software services.

    Reliability and observability

  • Incident handoff

    Incident handoff is a reliability concept used to describe a specific condition, control, or decision in the operation of software services.

    Reliability and observability

  • Incident impact assessment

    Incident impact assessment is a reliability concept used to describe a specific condition, control, or decision in the operation of software services.

    Reliability and observability

  • Incident recovery time

    Incident recovery time is a reliability concept used to describe a specific condition, control, or decision in the operation of software services.

    Reliability and observability

  • Incident reopen

    Incident reopen is a reliability concept used to describe a specific condition, control, or decision in the operation of software services.

    Reliability and observability

  • Incident response

    Incident response is the coordinated process of detecting, assessing, containing, communicating about, and recovering from an event that threatens a service or users. It includes the operational actions during the event and the learning work that follows.

    Reliability and observability

  • Incident response time

    Incident response time is a reliability concept used to describe a specific condition, control, or decision in the operation of software services.

    Reliability and observability

  • Incident severity

    Incident severity is a classification of the impact, urgency, and scope of a service incident. A severity level guides response priorities and communication; it is not a measure of personal fault.

    Reliability and observability

  • Incident severity matrix

    Incident severity matrix is a reliability concept used to describe a specific condition, control, or decision in the operation of software services.

    Reliability and observability

  • Incident stakeholder

    Incident stakeholder is a reliability concept used to describe a specific condition, control, or decision in the operation of software services.

    Reliability and observability

  • Incident status update

    Incident status update is a reliability concept used to describe a specific condition, control, or decision in the operation of software services.

    Reliability and observability

  • Incident-triggered deployment

    Incident-triggered deployment is a software delivery concept used to describe a specific event, interval, control, or operating condition in the path from source change to production behavior. A useful definition names the boundary, unit, and decision the measure supports.

    DORA and DevOps

  • Inclusive meeting

    Inclusive meeting is a way to organize, support, or evaluate software work so that teams can make useful progress with less avoidable friction. It is most valuable when connected to a concrete outcome and the local conditions of the team using it.

    Developer productivity

  • Incomplete data

    Incomplete data is an analytical risk or quality concern that can make an engineering analysis appear more certain, comparable, or causal than it is.

    Engineering analytics

  • Incremental refactoring

    Incremental refactoring is a software maintenance concern describing a condition that can make future changes, verification, operation, or ownership harder. Its practical importance depends on supported behavior, rate of change, and the consequences of delay.

    Code quality and technical debt

  • Individual productivity

    Individual productivity is a developer productivity concept that helps teams understand individual productivity in the context of software delivery.

    Developer productivity

  • Induction head

    Induction head is a language-model concept about evaluation design and failure analysis. It names a mechanism, representation, training practice, or operational behavior that can change how an AI system processes input and produces output.

    LLM fundamentals

  • Inference admission

    Inference admission is the serving concept concerned with inference admission during AI inference.

    Inference performance

  • Inference concurrency

    Inference concurrency is the serving concept concerned with inference concurrency during AI inference.

    Inference performance

  • Inference load shedding

    Inference load shedding is the serving concept concerned with inference load shedding during AI inference.

    Inference performance

  • Inference priority queue

    Inference priority queue is the serving concept concerned with inference priority queue during AI inference.

    Inference performance

  • Inference queue time

    Inference queue time is the serving concept concerned with inference queue time during AI inference.

    Inference performance

  • Inference throughput

    Inference throughput is the amount of model inference work completed in a period of time. It may be expressed as requests per second, input tokens per second, output tokens per second, or another workload-specific measure.

    Inference performance

  • Inference token budget

    Inference token budget is the serving concept concerned with inference token budget during AI inference.

    Inference performance

  • Inference unit cost

    Inference unit cost is the expense of one defined model-serving unit such as a request or workflow. It gives teams a way to name, measure, or reason about an economic property of an AI workload without treating raw usage as proof of value.

    Token costs and AI ROI

  • Infrastructure as code

    Infrastructure as code is a software delivery concept used to describe a specific event, interval, control, or operating condition in the path from source change to production behavior. A useful definition names the boundary, unit, and decision the measure supports.

    DORA and DevOps

  • Infrastructure debt

    Infrastructure debt is a software maintenance concern describing a condition that can make future changes, verification, operation, or ownership harder. Its practical importance depends on supported behavior, rate of change, and the consequences of delay.

    Code quality and technical debt

  • Infrastructure monitoring

    Infrastructure monitoring is observation of hosts, containers, networks, storage, and orchestration resources.

    Reliability and observability

  • Ingestion

    Ingestion brings source records into processing or storage.

    Engineering analytics

  • Inline function

    Inline function is a software maintenance concern describing a condition that can make future changes, verification, operation, or ownership harder. Its practical importance depends on supported behavior, rate of change, and the consequences of delay.

    Code quality and technical debt

  • Inline review comment

    Inline review comment is a practical concept in a pull-request workflow that shapes how people examine, discuss, own, or integrate a proposed change.

    Code review