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 termsDeveloper productivity flow state
Flow state is a developer productivity concept that helps teams understand flow state in the context of software delivery.
Developer productivityDeveloper productivity focus time
Focus time is a developer productivity concept that helps teams understand focus time in the context of software delivery.
Developer productivityDeveloper productivity forecast confidence
Forecast confidence is a developer productivity concept that helps teams understand forecast confidence in the context of software delivery.
Developer productivityDeveloper productivity handoff delay
Handoff delay is a developer productivity concept that helps teams understand handoff delay in the context of software delivery.
Developer productivityDeveloper productivity knowledge sharing
Knowledge sharing is a developer productivity concept that helps teams understand knowledge sharing in the context of software delivery.
Developer productivityDeveloper productivity sustainable pace
Sustainable pace is a developer productivity concept that helps teams understand sustainable pace in the context of software delivery.
Developer productivityDeveloper productivity task switching
Task switching is a developer productivity concept that helps teams understand task switching in the context of software delivery.
Developer productivityDeveloper productivity throughput forecast
Throughput forecast is a developer productivity concept that helps teams understand throughput forecast in the context of software delivery.
Developer productivityDeveloper self-service
Developer self-service is the ability for a software team to complete a supported engineering task through a platform or documented workflow without waiting for another team to perform routine steps. It includes clear ownership, safe defaults, and a recovery path when the standard route does not fit.
Developer productivityDeveloper velocity
Developer velocity is the rate at which a software team turns an idea into a reliable result while preserving a sustainable developer experience. It includes delivery flow, quality, feedback speed, and the conditions that help people do effective work.
Developer productivityDeveloper workload
Developer workload is a developer productivity concept that helps teams understand developer workload in the context of software delivery.
Developer productivityDevelopment set
Development set is a defined lens for examining AI system behavior with a stated task, evidence, and interpretation rule.
Evaluations and benchmarksDevOps change management
DevOps change management is a release engineering and DevOps concept for controlling how software changes are prepared, introduced, or understood.
DORA and DevOpsDiagnostic metric
Diagnostic metric helps explain why a headline outcome changed.
Engineering analyticsDifference in differences
Difference in differences 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 experimentationDirected acyclic graph
Directed acyclic graph 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 experimentationDirectional hypothesis
Directional 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 experimentationDisaggregated serving
Disaggregated serving is the serving concept concerned with disaggregated serving during AI inference.
Inference performanceDisaster recovery testing
Disaster recovery 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 debtDiscovery work
Discovery work 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 productivityDiscussion resolution
Discussion resolution is a practical concept in a pull-request workflow that shapes how people examine, discuss, own, or integrate a proposed change.
Code reviewDisk pressure
Disk pressure is a reliability concept used to describe a specific condition, control, or decision in the operation of software services.
Reliability and observabilityDistance from main sequence
An architecture signal that combines abstractness and instability to highlight potentially problematic component positions.
Code quality and technical debtDistributed trace
Distributed trace is a record following one operation across multiple processes or services with linked spans.
Reliability and observabilityDistributed tracing
Distributed tracing is a method for recording the path and timing of a request as it moves through multiple services, processes, or other components. A trace groups related spans that describe individual operations along that path.
Reliability and observabilityDivergent change
A code smell in which one module changes for many unrelated reasons.
Code quality and technical debtDocument representation
Document representation 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 fundamentalsDocumentation as code
Documentation as code 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 productivityDocumentation debt
Documentation 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 debtDocumentation review
Documentation review is a practical concept in a pull-request workflow that shapes how people examine, discuss, own, or integrate a proposed change.
Code reviewDomain adaptation
Domain adaptation 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 fundamentalsDORA metrics
DORA metrics are a set of software delivery performance measures used to understand how quickly and reliably an application changes in production. The current model covers change lead time, deployment frequency, failed deployment recovery time, change fail rate, and deployment rework rate.
DORA and DevOpsDownstream dependency
Downstream dependency is a concept used in software delivery planning to describe a condition, relationship, estimate, or decision about engineering work.
Flow and capacity planningDraft pull request
A draft pull request signals that a change is visible for early discussion but is not ready for final approval or merge.
Code reviewDrift detection
Drift detection watches for changes in a signal's distribution or meaning.
Engineering analyticsDry-run routing
Dry-run 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 gatewaysDual-environment deployment
Dual-environment deployment is a release engineering and DevOps concept for controlling how software changes are prepared, introduced, or understood.
DORA and DevOpsDuplicate events
Duplicate events is an analytical risk or quality concern that can make an engineering analysis appear more certain, comparable, or causal than it is.
Engineering analyticsDuplicate rate
Duplicate rate measures records repeated under identity and timing rules.
Engineering analyticsDuplicate request protection
Duplicate request protection 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 gatewaysDurable queue
Durable queue is a reliability concept used to describe a specific condition, control, or decision in the operation of software services.
Reliability and observabilityDynamic batching
Dynamic batching is the serving concept concerned with dynamic batching during AI inference.
Inference performanceDynamic routing
Dynamic 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 gatewaysEarly stopping
Early stopping 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 fundamentalsEarly stopping generation
Early stopping generation 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 fundamentalsEcological fallacy
Ecological fallacy 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 experimentationEdge-case set
Edge-case set is a defined lens for examining AI system behavior with a stated task, evidence, and interpretation rule.
Evaluations and benchmarksEffect size
Effect size 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 experimentationEffective AI rate
Effective AI rate is the realized cost per unit after discounts, caching, and routing adjustments. 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 ROIEffective capacity
Effective capacity is a concept used in software delivery planning to describe a condition, relationship, estimate, or decision about engineering work.
Flow and capacity planning