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

  • Estimate accuracy

    Estimate accuracy is a developer productivity concept that helps teams understand estimate accuracy in the context of software delivery.

    Developer productivity

  • Estimation

    Estimation 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

  • Estimation bias

    Estimation bias is a developer productivity concept that helps teams understand estimation bias in the context of software delivery.

    Developer productivity

  • Estimation uncertainty

    Estimation uncertainty is a developer productivity concept that helps teams understand estimation uncertainty in the context of software delivery.

    Developer productivity

  • Euclidean distance

    Euclidean distance 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

  • Evaluation case

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

    Evaluations and benchmarks

  • Evaluation criterion

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

    Evaluations and benchmarks

  • Evaluation dataset

    An evaluation dataset is a collection of cases used to assess a system against defined criteria. For an AI application, it can include inputs, expected behavior, reference answers, grading information, and the context needed to reproduce each case.

    Evaluations and benchmarks

  • Evaluation harness

    An evaluation harness is the software and configuration that runs an evaluation consistently. It typically loads cases, invokes a system, applies grading rules, records metrics, and produces results that can be compared across versions.

    Evaluations and benchmarks

  • Evaluation metric

    An evaluation metric is a defined calculation used to summarize how a system performs against an evaluation criterion. The calculation can compare predictions with references, classify outcomes, measure latency or cost, or combine several signals.

    Evaluations and benchmarks

  • Evaluation objective

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

    Evaluations and benchmarks

  • Evaluation plan

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

    Evaluations and benchmarks

  • Evaluation question

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

    Evaluations and benchmarks

  • Evaluation reproducibility

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

    Evaluations and benchmarks

  • Evaluation scenario

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

    Evaluations and benchmarks

  • Evaluation scope

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

    Evaluations and benchmarks

  • Evaluation versioning

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

    Evaluations and benchmarks

  • Event instrumentation

    Event instrumentation emits structured records for workflow transitions.

    Engineering analytics

  • Event name

    Event name identifies the action or state transition in a record.

    Engineering analytics

  • Event ordering

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

    Engineering analytics

  • Event property

    Event property adds named context used for filtering and diagnosis.

    Engineering analytics

  • Event reconciliation

    Event reconciliation how to reconcile event streams used by engineering metrics.

    Engineering analytics

  • Event schema

    Event schema defines fields, types, meanings, units, and version rules.

    Engineering analytics

  • Event versioning

    Event versioning manages changes to event meaning or shape safely.

    Engineering analytics

  • Event-based metric

    Event-based metric derives measures from timestamped actions or state changes.

    Engineering analytics

  • Example-based testing

    Example-based 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

  • Exemplar

    Exemplar is a sample observation attached to an aggregated metric point, often with trace context.

    Reliability and observability

  • Expand-contract migration

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

    DORA and DevOps

  • Expected calibration error

    Expected calibration error is a defined lens for examining AI system behavior with a stated task, evidence, and interpretation rule.

    Evaluations and benchmarks

  • Expedite lane

    An expedite lane is a class of service for work whose cost of waiting is unusually high. It is a policy with a small capacity allowance, not a general fast track for every request.

    Flow and capacity planning

  • Experiment-driven development

    Experiment-driven development 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

  • Experimental unit

    Experimental unit 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

  • Expert parallelism

    Expert parallelism is the serving concept concerned with expert parallelism during AI inference.

    Inference performance

  • Explicit work policies

    Explicit work policies are shared, observable agreements for workflow behavior. They can define entry criteria, WIP limits, service classes, pull rules, blocked handling, and completion conditions.

    Flow and capacity planning

  • Exploration work

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

    Developer productivity

  • Exponential backoff

    Exponential backoff 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

  • Exponential smoothing

    Exponential smoothing 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

  • Exporter

    Exporter sends collected observations to a destination.

    Engineering analytics

  • External benchmark

    External benchmark is an analytical concept for using reference values to understand engineering performance, variation, or capability.

    Engineering analytics

  • External dependency

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

    Flow and capacity planning

  • External validity

    External 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

  • Extract method

    Extract method 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

  • 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