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 F

54 terms

  • F1 score

    F1 score is a defined lens for examining AI system behavior with a stated task, evidence, and interpretation rule.

    Evaluations and benchmarks

  • Factory metric tree

    A factory metric tree is a structured map that connects a software factory goal to its contributing dimensions, measures, and source events. It helps teams move from a broad question about delivery or value to the specific evidence needed for investigation.

    Measurement and experimentation

  • Factuality evaluation

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

    Evaluations and benchmarks

  • Failed deployment recovery time

    Failed deployment recovery time measures how long it takes to recover from a deployment that fails and requires immediate intervention. Its scope is deployment-related failure, which makes it narrower than many general incident recovery or MTTR measures.

    DORA and DevOps

  • Failover routing

    Failover routing 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

  • Failover testing

    Failover 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

  • Failure domain

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

    Reliability and observability

  • Failure injection

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

    Reliability and observability

  • Fair queuing

    Fair queuing is the serving concept concerned with fair queuing during AI inference.

    Inference performance

  • Fairness policy

    Fairness policy 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

  • Fallback chain

    Fallback chain 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

  • Fallback reason

    Fallback reason 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

  • Fallback threshold

    Fallback threshold 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

  • False negative

    False negative 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

  • False positive

    False positive 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

  • Fast-forward merge

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

    Code review

  • Feature envy

    A code smell in which one method appears more interested in another object’s data than in its own context.

    Code quality and technical debt

  • Feature flags

    A feature flag is a runtime control that changes whether a code path is available without requiring a new deployment. Flags can support gradual rollout, experiments, emergency disablement, and safer integration of incomplete work.

    DORA and DevOps

  • Feature representation

    Feature 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 fundamentals

  • Feature toggle

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

    DORA and DevOps

  • Feature work

    Feature work is a developer productivity concept that helps teams understand feature work in the context of software delivery.

    Developer productivity

  • Feed-forward network

    Feed-forward network 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

  • Feedback coverage

    Feedback coverage is a developer productivity concept that helps teams understand feedback coverage in the context of software delivery.

    Developer productivity

  • Feedback latency

    Feedback latency is a developer productivity concept that helps teams understand feedback latency in the context of software delivery.

    Developer productivity

  • Feedback loop

    Feedback loop is a developer productivity concept that helps teams understand feedback loop in the context of software delivery.

    Developer productivity

  • Feedback quality

    Feedback quality is a developer productivity concept that helps teams understand feedback quality in the context of software delivery.

    Developer productivity

  • Few-shot prompting

    Few-shot prompting 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

  • Fishers exact test

    Fishers exact test 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

  • Five whys

    Five whys 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

  • Fixed versus variable AI costs

    Fixed versus variable AI costs is the distinction between stable expenses and usage-scaled expenses. 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

  • Fixed-date item

    A fixed-date item is a work item with a meaningful deadline or time window. It is a class of service that may justify different sequencing and forecasting, while still consuming the same constrained capacity.

    Flow and capacity planning

  • Flash attention

    Flash attention is the serving concept concerned with flash attention during AI inference.

    Inference performance

  • Flow age

    Flow age applies an explicit clock to unfinished work moving through a system. It helps teams focus attention on old items instead of relying only on statistics from completed work.

    Flow and capacity planning

  • Flow balance

    Flow balance describes whether arrival, WIP, and departure remain broadly aligned under defined policies. Imbalance appears as growing queues, starved stages, or repeated bursts of completed work.

    Flow and capacity planning

  • Flow constraint

    A flow constraint is a limiting policy, capability, dependency, or resource that reduces effective movement. A constraint can be structural or temporary and may move after an intervention.

    Flow and capacity planning

  • Flow control

    Flow control includes WIP limits, pull criteria, service classes, queue visibility, and feedback about age and completion. Its purpose is to prevent uncontrolled accumulation while preserving a safe path for work.

    Flow and capacity planning

  • Flow distribution

    Flow distribution is the spread of elapsed times for work items moving through a delivery process. It shows how often work finishes within different time ranges instead of reducing every item to one average.

    Flow and capacity planning

  • Flow efficiency

    Flow efficiency is the share of elapsed delivery time spent on active work rather than waiting, handoffs, or queues. It helps teams see whether a slow result comes from implementation effort or from delays around the work.

    Flow and capacity planning

  • Flow load

    Flow load is the volume of work placed on a workflow, including active and waiting items when the boundary is stated. It describes pressure on the system rather than effort or business value.

    Flow and capacity planning

  • Flow metrics

    Flow metrics include cycle time, throughput, work item age, queue time, and flow efficiency. Together they describe how work behaves rather than reducing engineering to one activity count.

    Flow and capacity planning

  • Flow policy

    A flow policy defines the behavior expected at a workflow boundary. It may specify entry criteria, WIP limits, service class handling, blocked work, and completion evidence.

    Flow and capacity planning

  • Flow predictability

    Flow predictability is the degree to which completed work matches a defined forecast, range, or service expectation over repeated observations. It concerns consistency, not simply speed.

    Flow and capacity planning

  • Flow rate

    Flow rate is the count of work items that pass a chosen completion boundary per unit of time. It is a throughput measure whose meaning depends on the item population and finish event.

    Flow and capacity planning

  • Flow review

    A flow review is a structured inspection of work movement, waiting, WIP, and delivery outcomes. Its purpose is to learn where the system is constrained and agree on a small change to test.

    Flow and capacity planning

  • Flow state

    Flow state 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

  • Flow time

    Flow time is the elapsed interval between a work item's entry into a measured workflow and its completion. It includes active processing and waiting inside the selected boundaries.

    Flow and capacity planning

  • Focus time

    Focus time 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

  • Forecast confidence

    Forecast confidence expresses how strongly available evidence supports a predicted delivery outcome. It should reflect variation, sample quality, work definition, and changes that could invalidate historical patterns.

    Flow and capacity planning

  • Forecast horizon

    A forecast horizon is the interval into the future for which a delivery prediction is intended to be useful. Its length depends on historical variation, work definition, system change, and the decision being supported.

    Flow and capacity planning

  • Forecast range

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

    Flow and capacity planning