A reference from Weave

Engineering & AI glossary

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

All terms

2,008 terms

  • 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

  • Inner loop efficiency

    Inner loop efficiency is a developer productivity concept that helps teams understand inner loop efficiency in the context of software delivery.

    Developer productivity

  • Input context

    Input context 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

  • Input metric

    Input metric describes a controllable activity or condition.

    Engineering analytics

  • Input token

    An input token is a unit of text or other encoded content sent to a language model before generation. The prompt, system instructions, conversation history, retrieved passages, and tool results can all contribute input tokens.

    Token costs and AI ROI

  • Input-output token mix

    Input-output token mix is the proportion of input and output tokens in a workload. 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

  • Instability metric

    A software architecture measure based on a component’s outgoing dependencies compared with its incoming dependencies.

    Code quality and technical debt

  • Instruction following

    Instruction following is a language-model concept about generation behavior and sampling. 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

  • Instruction hierarchy

    Instruction hierarchy 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

  • Instruction hierarchy conflict

    Instruction hierarchy conflict 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

  • Instruction-following evaluation

    Instruction-following evaluation is a defined lens for examining AI system behavior with a stated task, evidence, and interpretation rule.

    Evaluations and benchmarks

  • Instrumental variable

    Instrumental variable 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

  • Instrumentation

    Instrumentation adds hooks or measurements so behavior can be observed.

    Engineering analytics

  • Instrumentation bias

    Instrumentation bias 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

  • Instrumentation coverage

    Instrumentation coverage measures the observed share of relevant workflows or entities.

    Engineering analytics

  • Instrumentation drift

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

    Engineering analytics