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

  • Architecture decision latency

    The elapsed time between an architecture question being raised and a decision becoming usable for implementation.

    Code quality and technical debt

  • Architecture decision record

    A short document that captures an important architecture decision, its context, and its consequences.

    Code quality and technical debt

  • Architecture fitness function

    An automated or repeatable check that tests whether an architecture continues to meet a chosen constraint.

    Code quality and technical debt

  • Architecture review

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

    Code review

  • Architecture rework

    Engineering effort spent changing a structural design after implementation or adoption has begun.

    Code quality and technical debt

  • Arithmetic intensity

    Arithmetic intensity is the serving concept concerned with arithmetic intensity during AI inference.

    Inference performance

  • Arrival rate

    Arrival rate is the number of work items entering a defined workflow during a stated interval. It describes incoming demand, not the amount of work a team finishes.

    Flow and capacity planning

  • Artifact advancement

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

    DORA and DevOps

  • Artifact promotion

    Artifact promotion 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

  • Artifact version

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

    DORA and DevOps

  • Async inference

    Async inference is the serving concept concerned with async inference during AI inference.

    Inference performance

  • Async-first work

    Async-first work 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

  • Asynchronous code review

    Asynchronous code review lets authors and reviewers contribute at different times through a persistent change discussion rather than a shared meeting.

    Code review

  • Asynchronous communication

    Asynchronous communication 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

  • Atomic change

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

    Code review

  • Attention head

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

    LLM fundamentals

  • Attention optimization

    Attention optimization is the serving concept concerned with attention optimization during AI inference.

    Inference performance

  • Attribution bias

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

    Engineering analytics

  • Authentication proxy

    Authentication proxy is a model-routing or gateway concept used to manage interface consistency 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

  • Auto-instrumentation

    Auto-instrumentation is automatic addition of standard telemetry to supported libraries or runtimes.

    Reliability and observability

  • Autocorrelation

    Autocorrelation 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

  • Automated code review

    Automated code review uses deterministic tools or models to inspect a proposed change and report possible defects, policy violations, or maintainability issues.

    Code review

  • Automated deployment

    Automated 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

  • Automated Issue Resolution

    Automated Issue Resolution is a software-engineering concept describing how an AI coding system, its tools, or human collaborators handle a defined task.

    AI coding and agents

  • Automated refactoring

    Automated 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

  • Automatic instrumentation

    Automatic instrumentation adds telemetry through libraries or agents.

    Engineering analytics

  • Automatic prompt optimization

    Automatic prompt optimization 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

  • Autoscaling

    Autoscaling is the serving concept concerned with autoscaling during AI inference.

    Inference performance

  • Availability bias

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

    Engineering analytics

  • Availability testing

    Availability 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

  • Available capacity

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

    Flow and capacity planning

  • Average queue time

    Average queue time is the arithmetic mean of time spent waiting at a selected stage. It can summarize a queue, but a few old items may make the mean unlike the experience of most work.

    Flow and capacity planning

  • Average treatment effect

    Average treatment effect 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

  • Backdoor criterion

    Backdoor criterion 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

  • Backfill

    Backfill loads or recomputes historical records after a repair.

    Engineering analytics

  • Backlog aging

    Backlog aging is the elapsed time since a work item entered a backlog or became ready for consideration. It shows how long demand has waited before entering active delivery.

    Flow and capacity planning

  • Backlog health

    Backlog health is an assessment of whether queued work has enough clarity, relevance, and prioritization to support reliable replenishment. It is a judgment supported by measures rather than a single universal score.

    Flow and capacity planning

  • Backlog refinement

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

    Flow and capacity planning

  • Backpressure

    Backpressure 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

  • Backup integrity

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

    Reliability and observability

  • Backup retention

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

    Reliability and observability

  • Backward compatibility

    Backward compatibility 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

  • Backward-compatible change

    Backward-compatible change is a release engineering and DevOps concept for controlling how software changes are prepared, introduced, or understood.

    DORA and DevOps

  • Baggage

    Baggage is request-scoped key-value context propagated across service boundaries.

    Reliability and observability

  • Baseline regression

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

    Engineering analytics

  • Batch inference

    Batch inference processes multiple model inputs together in one serving operation. Grouping requests can improve hardware utilization, but it may add waiting time while a batch fills and must account for different input and output lengths.

    Inference performance

  • Batch padding

    Batch padding is the serving concept concerned with batch padding during AI inference.

    Inference performance

  • Batch size

    Batch size is the amount of work grouped into one processing, review, release, or handoff unit. The unit may be a change, pull request, story, deployment, or set of requests.

    Flow and capacity planning

  • Batch wait

    Batch wait is the elapsed delay caused by holding work until a batch threshold, calendar window, or group of related items is ready. It is a queue effect created by batching policy.

    Flow and capacity planning

  • Beam search decoding

    Beam search decoding is a language-model concept about instruction design and control. 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