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

  • 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

  • Control chart

    Control chart 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

  • Control group

    Control group 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

  • Control-flow graph

    A graph that represents the possible paths of execution through a program.

    Code quality and technical debt

  • Conversation history

    Conversation history 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

  • Coordination cost

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

    Developer productivity

  • Corrective release

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

    DORA and DevOps

  • Correlation and causation

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

    Engineering analytics

  • Correlation ID

    Correlation ID associates related records across systems.

    Engineering analytics

  • Cosine similarity

    Cosine similarity 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

  • Cost evaluation

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

    Evaluations and benchmarks

  • Cost of delay

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

    Flow and capacity planning

  • Cost per agent run

    Cost per agent run is model expense incurred by one execution of an agent 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

  • Cost per AI evaluation

    Cost per AI evaluation is expense of evaluating one response, task, or candidate system. 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

  • Cost per AI review

    Cost per AI review is the model expense required to produce one code or document review. 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

  • Cost per AI session

    Cost per AI session is the average AI expense associated with one user or agent session. 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

  • Cost per AI test run

    Cost per AI test run is model expense for generating, selecting, or analyzing one test run. 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

  • Cost per AI user

    Cost per AI user is the average AI expense associated with a user over a period. 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

  • Cost per AI workflow

    Cost per AI workflow is the average model expense for one defined multi-step workflow. 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

  • Cost per code change

    Cost per code change is AI expense associated with producing a code change that reaches an agreed state. 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

  • Cost per completed task

    Cost per completed task is the total cost of attempting a workload divided by the number of tasks that meet its completion criteria. For AI workflows, it can include model calls, retries, tool execution, and other costs within the stated measurement boundary.

    Token costs and AI ROI

  • Cost per generated artifact

    Cost per generated artifact is the average expense for one accepted patch, summary, or report. 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

  • Cost per resolved issue

    Cost per resolved issue is model spend associated with resolving one software or support issue. 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

  • Cost per successful AI outcome

    Cost per successful AI outcome is the AI expense required for an agreed successful result. 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

  • Cost per token

    Cost per token is the price charged for processing a defined number of input or output tokens. Providers commonly quote separate input and output rates, and some offer lower prices for cached or batched work.

    Token costs and AI ROI

  • Count metric

    Count metric records how many defined events or entities occur.

    Engineering analytics

  • Counter metric

    Counter metric is a value that increases as occurrences happen, such as requests, jobs, or errors.

    Reliability and observability

  • Counterfactual

    Counterfactual 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

  • Counterfactual set

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

    Evaluations and benchmarks

  • Coupling between objects

    A count of relationships a class or object has with other classes or objects.

    Code quality and technical debt

  • Covariate

    Covariate 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

  • CPU throttling

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

    Reliability and observability

  • Credential injection

    Credential injection 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

  • Criterion-based evaluation

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

    Evaluations and benchmarks

  • Critical chain

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

    Flow and capacity planning

  • Critical path

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

    Flow and capacity planning