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

  • Critical path analysis

    Critical path analysis is identification of dependent work that determines when an operation can complete.

    Reliability and observability

  • Critical path method

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

    Flow and capacity planning

  • Cross sectional study

    Cross sectional study 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

  • Cross-attention

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

    LLM fundamentals

  • Cross-browser testing

    Cross-browser 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

  • Cross-functional team

    Cross-functional team 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

  • Cross-region failover

    Cross-region failover 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

  • Cross-team dependency

    Cross-team dependency is a concept used in software delivery planning to describe a condition, relationship, estimate, or decision about engineering work.

    Flow and capacity planning

  • Cumulative flow diagram

    A cumulative flow diagram shows how many work items occupy each workflow state over time. The thickness of a band represents the amount of work in that state, while the spacing between boundaries helps reveal movement and waiting.

    Flow and capacity planning

  • Cumulative metric

    Cumulative metric shows a running total across time or population.

    Engineering analytics

  • Curriculum learning

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

    LLM fundamentals

  • Customer feedback loop

    Customer feedback loop 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

  • Customer impact window

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

    Reliability and observability

  • Cycle time

    Cycle time is the elapsed time between a work item's defined start and finish. In software delivery, its meaning depends on the workflow boundaries, such as development started to deployed, or pull request opened to merged.

    Flow and capacity planning

  • Cycle time forecast

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

    Flow and capacity planning

  • Cycle time percentile

    A cycle time percentile is a value below which a chosen percentage of completed work falls. For example, a 90th percentile cycle time of five days means 90 percent of the measured items completed in five days or less.

    Flow and capacity planning

  • Cyclomatic complexity density

    Cyclomatic complexity normalized by a measure of code size.

    Code quality and technical debt

  • Dark launch

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

    DORA and DevOps

  • Dashboard

    Dashboard curates measures, context, and navigation for a decision.

    Engineering analytics

  • Dashboard hygiene

    Dashboard hygiene keeps metric views accurate, current, and understandable.

    Engineering analytics

  • Data accuracy

    Data accuracy asks whether records reflect the real event or value.

    Engineering analytics

  • Data availability

    Data availability measures whether an expected data asset can be accessed and used.

    Engineering analytics

  • Data catalog

    Data catalog inventories assets with owners, schemas, lineage, freshness, and access.

    Engineering analytics

  • Data collector

    Data collector receives, processes, and forwards observations.

    Engineering analytics

  • Data consistency

    Data consistency keeps related fields and records compatible across sources.

    Engineering analytics

  • Data contamination

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

    Evaluations and benchmarks

  • Data contract

    Data contract agrees schema, meaning, quality, ownership, and change handling.

    Engineering analytics

  • Data contract testing

    Data contract testing how to test a data contract before it breaks analytics.

    Engineering analytics

  • Data debt

    Data 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

  • Data deduplication

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

    LLM fundamentals

  • Data dictionary

    Data dictionary documents field meanings, types, units, and allowed values.

    Engineering analytics

  • Data filtering

    Data filtering 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

  • Data lineage

    Data lineage traces a result through sources, joins, transformations, and presentation.

    Engineering analytics

  • Data migration

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

    DORA and DevOps

  • Data mixture

    Data mixture 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

  • Data mixture weighting

    Data mixture weighting 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

  • Data observability

    Data observability monitors freshness, volume, schema, distribution, and quality behavior.

    Engineering analytics

  • Data parallelism

    Data parallelism is the serving concept concerned with data parallelism during AI inference.

    Inference performance

  • Data quality

    Data quality 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

  • Data reconciliation

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

    Reliability and observability

  • Data residency routing

    Data residency 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

  • Data timeliness

    Data timeliness measures whether data arrives in time for its decision.

    Engineering analytics

  • Data validity

    Data validity checks permitted structure, type, range, format, and rules.

    Engineering analytics

  • Data warehouse

    Data warehouse combines operational records for historical analysis.

    Engineering analytics

  • Data-flow analysis

    A static-analysis technique that tracks how values move through a program.

    Code quality and technical debt

  • Database backfill

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

    DORA and DevOps

  • Database migration

    Database migration 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

  • Dead code

    Code that cannot execute or whose result is never needed by the program.

    Code quality and technical debt

  • Dead letter queue

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

    Reliability and observability

  • Deadline propagation

    Deadline propagation 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