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

  • Request coalescing

    Request coalescing is the serving concept concerned with request coalescing during AI inference.

    Inference performance

  • Request correlation ID

    Request correlation ID 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

  • Request normalization

    Request normalization 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

  • Request priority

    Request priority is a model-routing or gateway concept used to manage policy enforcement 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

  • Request rate

    Request rate is the serving concept concerned with request rate during AI inference.

    Inference performance

  • Request scheduler

    Request scheduler is the serving concept concerned with request scheduler during AI inference.

    Inference performance

  • Requested changes

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

    Code review

  • Required review

    Required review is a repository or branch rule that blocks integration until specified review conditions are met.

    Code review

  • Research hypothesis

    Research hypothesis 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

  • Residual connection

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

    LLM fundamentals

  • Resilience budget

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

    Reliability and observability

  • Resilience debt

    Resilience 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

  • Resilience testing

    Resilience 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

  • Resource attribute

    Resource attribute is metadata identifying the entity that produced telemetry, such as service, host, process, or environment.

    Reliability and observability

  • Resource exhaustion

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

    Reliability and observability

  • Response cache

    Response cache is the serving concept concerned with response cache during AI inference.

    Inference performance

  • Response for a class

    A measure of the number of methods potentially executed in response to a message received by a class.

    Code quality and technical debt

  • Response normalization

    Response normalization 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

  • Response prefilling

    Response prefilling 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

  • Restore validation

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

    Reliability and observability

  • Retrieval augmented generation

    Retrieval augmented generation, or RAG, is an architecture that retrieves relevant documents or records and supplies them to a language model as context for generating a response. It can ground answers in a changing or private information source without retraining the model for every update.

    LLM fundamentals

  • Retrieval evaluation

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

    Evaluations and benchmarks

  • Retrospective action

    Retrospective action 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

  • Retry amplification

    Retry amplification is the serving concept concerned with retry amplification during AI inference.

    Inference performance

  • Retry budget

    Retry budget 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

  • Retry policy

    Retry policy 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

  • Retryable error

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

    Reliability and observability

  • Revert review

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

    Code review

  • Review abandonment

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

    Code review

  • Review backlog

    A code review backlog is the accumulated set of proposed changes that still require review activity or a final decision.

    Code review

  • Review bottleneck

    A review bottleneck is a constrained person, team, policy, or stage that limits the movement of proposed changes.

    Code review

  • Review capacity

    Review capacity is the effective ability of reviewers to assess eligible changes during a period. It depends on change scope, reviewer context, specialist requirements, interruptions, and the standard of review expected.

    Flow and capacity planning

  • Review capacity buffer

    A review capacity buffer is deliberately uncommitted reviewer capacity reserved for incoming changes, urgent work, and variation in review difficulty.

    Code review

  • Review capacity planning

    Review capacity planning is the practice of matching expected code review demand with the people, ownership, time, and knowledge needed to provide timely and effective review. It treats review as a delivery capability rather than spare work performed after implementation.

    Developer productivity

  • Review comment

    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

  • Review comment density

    Review comment density is the number of review comments relative to a chosen unit such as changed lines, files, or pull requests.

    Code review

  • Review comment resolution

    Review comment resolution tracks whether feedback receives a response, code change, explicit rationale, or documented decision.

    Code review

  • Review coverage

    Review coverage describes the share of eligible changes that receive the intended review evidence before integration.

    Code review

  • Review cycle count

    Review cycle count is the number of meaningful feedback and revision cycles a change passes through before its review is complete.

    Code review

  • Review defect detection

    Review defect detection is the practice of finding correctness, security, reliability, or maintainability problems before code reaches later stages.

    Code review

  • Review depth

    Review depth describes how carefully a change is examined across behavior, design, tests, security, and maintainability.

    Code review

  • Review effectiveness

    Review effectiveness is the extent to which a review finds useful problems, improves shared understanding, and supports a safe change.

    Code review

  • Review finding rate

    Review finding rate is the frequency at which a review identifies a defined issue class before a change is integrated.

    Code review

  • Review handoff

    A code review handoff transfers responsibility for understanding and progressing a proposed change from one reviewer or team to another.

    Code review

  • Review iteration

    A review iteration is one cycle of author changes followed by reviewer feedback on the same proposed change.

    Code review

  • Review iteration count

    Review iteration count is a developer productivity concept that helps teams understand review iteration count in the context of software delivery.

    Developer productivity

  • Review load distribution

    Review load distribution shows how review requests or completed reviews are spread across people, teams, repositories, or change types.

    Code review

  • Review meeting

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

    Code review

  • Review noise

    Review noise is feedback or workflow activity that consumes attention without helping a reviewer or author make a safer, clearer change.

    Code review

  • Review objective

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

    Code review