A reference from Weave

Find a term

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

  • Compliance routing

    Compliance routing 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

  • Component testing

    Component 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

  • Composite score

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

    Evaluations and benchmarks

  • Compute bound inference

    Compute bound inference is the serving concept concerned with compute bound inference during AI inference.

    Inference performance

  • Concurrency limit

    Concurrency limit 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

  • Confidence interval

    A confidence interval is a range produced by a statistical procedure to estimate an unknown population parameter. Its confidence level describes the procedure's long-run coverage under its assumptions, rather than the probability that a fixed parameter lies inside one observed interval.

    Measurement and experimentation

  • Confidence level

    Confidence level 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

  • Confidence score

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

    Evaluations and benchmarks

  • Configuration as code

    Configuration as code is a release engineering and DevOps concept for controlling how software changes are prepared, introduced, or understood.

    DORA and DevOps

  • Configuration drift

    Configuration drift 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

  • Configuration drift debt

    Configuration drift 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

  • Configuration reload

    Configuration reload 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

  • Configuration state drift

    Configuration state drift 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

  • Configuration validation

    Configuration validation 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

  • Confirmation bias

    Confirmation 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

  • Connect timeout

    Connect 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

  • Connection draining

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

    DORA and DevOps

  • Connection pool exhaustion

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

    Reliability and observability

  • Consistency check

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

    Reliability and observability

  • Consistent hashing routing

    Consistent hashing 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

  • Constrained decoding

    Constrained decoding 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

  • Construct validity

    Construct validity 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

  • Consumer-driven contract

    Consumer-driven contract is a release engineering and DevOps concept for controlling how software changes are prepared, introduced, or understood.

    DORA and DevOps

  • Context length

    Context length 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

  • Context length routing

    Context length 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

  • Context overhead

    Context overhead is tokens carrying history, retrieved material, tools, or metadata around a task. 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

  • Context packing

    Context packing 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

  • Context propagation

    Context propagation is the transport of correlation information across processes, threads, services, and asynchronous work.

    Reliability and observability

  • Context ranking

    Context ranking 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

  • Context selection

    Context selection 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

  • Context switching cost

    Context switching cost is a developer productivity concept that helps teams understand context switching cost in the context of software delivery.

    Developer productivity

  • Context truncation

    Context truncation 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

  • Context window

    A context window is the amount of information a language model can consider within a request and its generation process, usually expressed in tokens. The applicable limits and accounting rules depend on the model and serving interface.

    LLM fundamentals

  • Context window utilization

    Context window utilization 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

  • Contextual instruction

    Contextual instruction 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

  • Contingency plan

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

    Flow and capacity planning

  • Continued pretraining

    Continued pretraining 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

  • Continuous batching

    Continuous batching is the serving concept concerned with continuous batching during AI inference.

    Inference performance

  • Continuous delivery

    Continuous delivery is a software development approach in which changes are kept in a releasable state through automated build, test, and delivery practices. A production release may still require a human decision.

    DORA and DevOps

  • Continuous deployment

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

    DORA and DevOps

  • Continuous flow

    Continuous flow is a way of managing work in which items are pulled and completed individually or in small increments, with attention to queues and capacity rather than a fixed iteration boundary.

    Flow and capacity planning

  • Continuous improvement

    Continuous improvement 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

  • Continuous integration

    Continuous integration is the practice of merging small code changes into a shared branch frequently and validating them with automated builds and tests. Its value comes from early feedback, not from running a pipeline on a schedule alone.

    DORA and DevOps

  • Continuous integration practice

    Continuous integration practice is a release engineering and DevOps concept for controlling how software changes are prepared, introduced, or understood.

    DORA and DevOps

  • Continuous quality testing

    Continuous quality testing 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

  • Continuous testing

    Continuous 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

  • Continuous testing practice

    Continuous testing practice is a release engineering and DevOps concept for controlling how software changes are prepared, introduced, or understood.

    DORA and DevOps

  • Contract-first development

    Contract-first development is a release engineering and DevOps concept for controlling how software changes are prepared, introduced, or understood.

    DORA and DevOps

  • Contrastive decoding

    Contrastive 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

  • Contrastive learning

    Contrastive learning 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