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

  • Statistical process control

    Statistical process control 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

  • Step-back prompting

    Step-back 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

  • Sticky routing

    Sticky 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 gateways

  • Story points

    Story points 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

  • Strangler fig pattern

    Strangler fig pattern 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

  • Stratified sampling

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

    Evaluations and benchmarks

  • Stream-aligned team

    Stream-aligned 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

  • Streaming inference

    Streaming inference is the serving concept concerned with streaming inference during AI inference.

    Inference performance

  • Streaming normalization

    Streaming normalization 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

  • Stress testing

    Stress 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

  • Structured logging

    Structured logging is writing log records as named fields instead of only free-form text.

    Reliability and observability

  • Structured output

    Structured output is a model response constrained to a defined shape such as JSON with named fields and types. It gives application code a predictable contract while leaving the model responsible for generating the field values.

    LLM fundamentals

  • Structured output normalization

    Structured output normalization 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

  • Substantive review comment

    A substantive review comment identifies a behavior, risk, question, or improvement that could change the author's decision or understanding.

    Code review

  • Subsystem testing

    Subsystem 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

  • Summary metric

    Summary metric is a client-side statistical summary of observations, commonly count, sum, and selected quantiles.

    Reliability and observability

  • Superposition

    Superposition 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

  • Supply chain risk

    Supply chain risk 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

  • Survivorship bias

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

    Engineering analytics

  • Sustainable pace

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

    Flow and capacity planning

  • SUTVA

    SUTVA 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

  • SWE Bench

    SWE Bench is a software-engineering concept describing how an AI coding system, its tools, or human collaborators handle a defined task.

    AI coding and agents

  • Synchronous communication

    Synchronous communication 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

  • Synchronous review

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

    Code review

  • Synthetic monitoring

    Synthetic monitoring is scheduled scripted requests or workflows run from controlled conditions.

    Reliability and observability

  • System testing

    System testing evaluates a complete integrated software system against specified requirements. It exercises the system through intended interfaces and considers whether the assembled product behaves correctly in a representative environment.

    Code quality and technical debt

  • T-test

    T-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

  • Tail latency

    Tail latency is the serving concept concerned with tail latency during AI inference.

    Inference performance

  • Tail-based sampling

    Tail-based sampling is a sampling decision made after later spans and overall trace outcome are available.

    Reliability and observability

  • Target date

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

    Flow and capacity planning

  • Task batching

    Task batching is a developer productivity concept that helps teams understand task batching in the context of software delivery.

    Developer productivity

  • Task completion

    Task completion is a developer productivity concept that helps teams understand task completion in the context of software delivery.

    Developer productivity

  • Task completion rate

    Task completion rate is a developer productivity concept that helps teams understand task completion rate in the context of software delivery.

    Developer productivity

  • Task Decomposition

    Task Decomposition is a software-engineering concept describing how an AI coding system, its tools, or human collaborators handle a defined task.

    AI coding and agents

  • Task decomposition prompting

    Task decomposition prompting 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

  • Task difficulty

    Task difficulty is a developer productivity concept that helps teams understand task difficulty in the context of software delivery.

    Developer productivity

  • Task specification

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

    Evaluations and benchmarks

  • Task switching

    Task switching 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

  • Task type routing

    Task type 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 gateways

  • Team capacity

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

    Flow and capacity planning

  • Team charter

    Team charter 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

  • Team Comparison

    Team Comparison is an analytical concept for separating engineering observations into populations whose differences may matter to a decision.

    Engineering analytics

  • Team interaction mode

    Team interaction mode 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

  • Team productivity

    Team productivity is a developer productivity concept that helps teams understand team productivity in the context of software delivery.

    Developer productivity

  • Team Topologies

    Team Topologies 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

  • Team topology segment

    Team topology segment is an analytical concept for separating engineering observations into populations whose differences may matter to a decision.

    Engineering analytics

  • Technical debt

    Technical debt is the future cost created by technical choices that make software harder to change, operate, or understand. The debt metaphor distinguishes the work needed to improve the design from the recurring friction of leaving it as it is.

    Code quality and technical debt

  • Technical debt prioritization

    The practice of ordering debt work using impact, urgency, effort, and evidence.

    Code quality and technical debt

  • Technical debt ratio

    A ratio that compares estimated remediation cost with the cost or value of the related software asset or scope.

    Code quality and technical debt

  • Technical debt register

    A maintained list of known technical debt items with context, impact, ownership, and next action.

    Code quality and technical debt