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

  • Pull request size

    Pull request size describes the amount of change contained in a pull request. Tools may count added and deleted lines, changed files, commits, or a combination, so a size report should state its unit.

    Code review

  • Pull request to production time

    Pull request 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

  • Pull request waiting time

    Pull request waiting time is a developer productivity concept that helps teams understand pull request waiting time in the context of software delivery.

    Developer productivity

  • Pull system

    A pull system starts or advances work when the receiving stage has capacity and the item meets its policy. Pulling controls WIP and makes the decision to begin work visible.

    Flow and capacity planning

  • Quality evaluation

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

    Evaluations and benchmarks

  • Quality gate

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

    DORA and DevOps

  • Quality profile

    A named collection of analysis rules and settings applied to a codebase or language.

    Code quality and technical debt

  • Quality-adjusted AI cost

    Quality-adjusted AI cost is effective expense after accounting for quality or acceptance. 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

  • Quantization

    Quantization represents model values with lower numerical precision than the original model. It can reduce memory requirements and sometimes improve serving efficiency, with possible effects on output quality and supported operations.

    Inference performance

  • Quantization aware serving

    Quantization aware serving is the serving concept concerned with quantization aware serving during AI inference.

    Inference performance

  • Quartile

    Quartile 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

  • Quasi experiment

    Quasi experiment 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

  • Queue age

    Queue age measures how long an item has waited for the next processing opportunity. It is distinct from queue depth, which counts how many items are waiting at a snapshot.

    Flow and capacity planning

  • Queue backpressure

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

    Reliability and observability

  • Queue capacity

    Queue capacity is the allowed or practical amount of waiting work for a defined stage. It may be expressed as a WIP limit, a service slot, or an operating range.

    Flow and capacity planning

  • Queue depth

    Queue depth is the count of items waiting for the same processing step, capacity slot, approval, or dependency at a particular time. It is a snapshot of accumulation, not a measure of how long items have waited.

    Flow and capacity planning

  • Queue drain

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

    Reliability and observability

  • Queue latency

    Queue latency is the serving concept concerned with queue latency during AI inference.

    Inference performance

  • Queue stability

    Queue stability describes the pattern of queue depth and age over time. A stable queue may still be undesirable if it is permanently large, while an unstable queue signals changing arrival, service capacity, batching, or policy.

    Flow and capacity planning

  • Queue time

    Queue time is the elapsed period when work is ready to move but is waiting for a person, system, approval, capacity slot, or dependency. It is a component of total cycle time and often reveals hidden delivery constraints.

    Flow and capacity planning

  • Quorum

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

    Reliability and observability

  • RAG evaluation

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

    Evaluations and benchmarks

  • Random assignment

    Random assignment 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

  • Random routing

    Random routing is a model-routing or gateway concept used to manage traffic selection 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

  • Random sampling

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

    Evaluations and benchmarks

  • Random seed

    Random seed 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

  • Randomization

    Randomization 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

  • Randomized controlled trial

    Randomized controlled trial 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

  • Range

    Range 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

  • Rate limit bucket

    Rate limit bucket 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

  • Rate metric

    Rate metric relates event frequency to population or exposure.

    Engineering analytics

  • Ratio metric

    Ratio metric compares two related quantities.

    Engineering analytics

  • Read timeout

    Read timeout 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

  • Readiness gate

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

    Reliability and observability

  • Readiness probe

    Readiness probe is a test of whether a service is prepared to receive traffic or work now.

    Reliability and observability

  • Real user monitoring

    Real user monitoring is performance and interaction measurement collected from actual user sessions.

    Reliability and observability

  • Reasoning trace

    Reasoning trace is a language-model concept about representation and similarity. 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

  • Rebase before review

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

    Code review

  • Rebase merge

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

    Code review

  • Recall

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

    Evaluations and benchmarks

  • Reconciliation

    Reconciliation compares related totals or states across systems.

    Engineering analytics

  • Reconciliation loop

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

    DORA and DevOps

  • Recovery drill

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

    Reliability and observability

  • Recovery point objective

    Recovery point objective 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

  • Recovery testing

    Recovery testing is a software testing or test-design practice used to gather evidence about a defined risk, behavior, boundary, or operating condition. It makes the question under test explicit, identifies the inputs and observations that matter, and gives a team a repeatable basis for deciding whether the result is acceptable.

    Code quality and technical debt

  • Recovery time objective

    Recovery time objective 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

  • Recreate deployment

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

    DORA and DevOps

  • Refactoring backlog

    Refactoring backlog 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

  • Refactoring safety

    The confidence that a structural code change preserves externally observable behavior.

    Code quality and technical debt

  • Reference answer

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

    Evaluations and benchmarks