A reference from Weave

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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 C

252 terms

  • Cache eviction

    Cache eviction is the serving concept concerned with cache eviction during AI inference.

    Inference performance

  • Cache hit rate

    Cache hit rate is the serving concept concerned with cache hit rate during AI inference.

    Inference performance

  • Cache invalidation

    Cache invalidation is the serving concept concerned with cache invalidation during AI inference.

    Inference performance

  • Cache key

    Cache key is the serving concept concerned with cache key during AI inference.

    Inference performance

  • Cache ttl

    Cache ttl is the serving concept concerned with cache ttl during AI inference.

    Inference performance

  • Cache warming

    Cache warming is the serving concept concerned with cache warming during AI inference.

    Inference performance

  • Cached token ratio

    Cached token ratio is the share of input tokens served from a reusable context cache. 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

  • Calibration error

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

    Evaluations and benchmarks

  • Calibration set

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

    Evaluations and benchmarks

  • Canary deployment

    Canary 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

  • Canary rollout

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

    DORA and DevOps

  • Canary routing

    Canary 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

  • Capability routing

    Capability routing is a model-routing or gateway concept used to manage operational visibility 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

  • Capacity allocation

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

    Flow and capacity planning

  • Capacity buffer

    A capacity buffer is intentionally uncommitted capacity held available for variation or work that cannot be forecast precisely. It is a planning policy, not evidence that people should remain idle.

    Flow and capacity planning

  • Capacity constraint

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

    Flow and capacity planning

  • Capacity forecast

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

    Flow and capacity planning

  • Capacity headroom

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

    Reliability and observability

  • Capacity planning

    Capacity planning is the practice of estimating the work a delivery system can complete and comparing it with expected demand, constraints, and service commitments. It supports tradeoffs rather than promising exact output.

    Flow and capacity planning

  • Capacity reserve

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

    Flow and capacity planning

  • Capacity scenario

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

    Flow and capacity planning

  • Capacity utilization

    Capacity utilization is the ratio of capacity used for a defined class of work to the capacity available for that class during the same interval. It is meaningful only when both numerator and denominator are defined consistently.

    Flow and capacity planning

  • Capacity variance

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

    Flow and capacity planning

  • Capacity-constrained flow

    Capacity-constrained flow occurs when a stage, skill, environment, or policy has less effective capacity than the demand arriving at it. The result is usually a growing queue, longer waiting, or reduced throughput.

    Flow and capacity planning

  • Cardinality management

    Cardinality management is control of distinct attribute combinations so telemetry remains queryable and affordable.

    Reliability and observability

  • Catastrophic forgetting

    Catastrophic forgetting 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

  • Causal diagram

    Causal diagram 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

  • Causal language modeling

    Causal language modeling 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

  • Causal mask

    Causal mask 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

  • Cause-effect graphing

    Cause-effect graphing 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

  • Central limit theorem

    Central limit theorem 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

  • Chain-of-thought prompting

    Chain-of-thought prompting 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

  • Challenge set

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

    Evaluations and benchmarks

  • Change adoption

    Change adoption 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

  • Change approval

    Change approval 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

  • Change approval time

    Change approval 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

  • Change author

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

    Code review

  • Change failure

    A change failure is a production change that causes an incident, service degradation, rollback, hotfix, or another defined operational problem. The organization must define which outcomes count before calculating a rate.

    DORA and DevOps

  • Change failure analysis

    Change failure analysis is the structured examination of a software change that caused a production failure or required immediate intervention. It connects the change, failure mode, user impact, detection, response, and prevention work so the delivery system can learn.

    DORA and DevOps

  • Change failure definition

    Change failure definition is a release engineering and DevOps concept for controlling how software changes are prepared, introduced, or understood.

    DORA and DevOps

  • Change failure queue

    A change failure queue is the set of failed or suspect changes that still require operational follow-up. It describes unfinished recovery work, not merely the count of failed deployments.

    Flow and capacity planning

  • Change failure rate

    Change failure rate is the proportion of production deployments that require immediate corrective intervention, such as a rollback or urgent fix. It relates failures to deployed changes instead of counting every incident affecting a service.

    DORA and DevOps

  • Change failure rate definition

    Change failure rate definition is a release engineering and DevOps concept for controlling how software changes are prepared, introduced, or understood.

    DORA and DevOps

  • Change lead time

    Change lead time is the elapsed time between a code change being committed to version control and that change being deployed in production. It describes a specific portion of software delivery, rather than the full time from an idea to a customer outcome.

    DORA and DevOps

  • Change lead time definition

    Change lead time definition is a release engineering and DevOps concept for controlling how software changes are prepared, introduced, or understood.

    DORA and DevOps

  • Change management

    Change management 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

  • Change point

    Change point 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

  • Change request

    A change request is reviewer feedback that asks the author to modify a proposed change before it can be accepted.

    Code review

  • Change retry rate

    Change retry rate 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

  • Change scope

    Change scope is a developer productivity concept that helps teams understand change scope in the context of software delivery.

    Developer productivity