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
  • Semantic layer

    Semantic layer models governed concepts above raw data.

    Engineering analytics
  • Semantic routing

    Semantic 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
  • Semantic versioning

    Semantic versioning 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
  • Sensitivity analysis

    Sensitivity analysis 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
  • Sentence representation

    Sentence representation 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
  • Separation of concerns

    The practice of organizing software so distinct responsibilities can change and be reasoned about independently.

    Code quality and technical debt
  • Sequence packing

    Sequence packing is the serving concept concerned with sequence packing during AI inference.

    Inference performance
  • Server sent events

    Server sent events is the serving concept concerned with server sent events during AI inference.

    Inference performance
  • Service catalog

    Service catalog 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
  • Service class

    A service class is a category of work with a shared policy for sequencing, WIP treatment, and service expectation. Examples include standard, expedite, fixed-date, and intangible work.

    Flow and capacity planning
  • Service Comparison

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

    Engineering analytics
  • Service discovery for models

    Service discovery for models is a model-routing or gateway concept used to manage operational visibility 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
  • Service extraction

    Service extraction 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
  • Service level agreement

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

    Reliability and observability
  • Service level credit

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

    Reliability and observability
  • Service level indicator

    A service level indicator, or SLI, is a carefully defined quantitative measure of a service behavior that matters to users. Common examples include availability, request latency, error rate, and throughput.

    Reliability and observability
  • Service level objective

    A service level objective, or SLO, is a target value or range for a service level indicator. It states the level of service a team aims to provide over a defined period and measurement boundary.

    Reliability and observability
  • Service map

    Service map is a representation of services and observed communication paths between them.

    Reliability and observability
  • Service ownership

    Service ownership is a software delivery concept used to describe a specific event, interval, control, or operating condition in the path from source change to production behavior. A useful definition names the boundary, unit, and decision the measure supports.

    DORA and DevOps
  • Service rate

    Service rate is the number of items that a stage or system completes per unit of time. It is a measured outcome of capacity, work mix, policies, and variation, not a fixed property of a person.

    Flow and capacity planning
  • Service template

    A service template is a reusable starting point for creating a software service and its supporting configuration. It may include repository structure, build and deployment workflows, observability, security controls, documentation, and defaults that teams can adapt to their context.

    Measurement and experimentation
  • Service time

    Service time is the serving concept concerned with service time during AI inference.

    Inference performance
  • Serving backpressure

    Serving backpressure is the serving concept concerned with serving backpressure during AI inference.

    Inference performance
  • Shadow routing

    Shadow 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
  • Shadow traffic

    Shadow traffic 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
  • Shared fate

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

    Reliability and observability
  • Shared understanding

    Shared understanding 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
  • Shift-left testing

    Shift-left 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
  • Shift-right testing

    Shift-right 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
  • Shotgun surgery

    A code smell in which one conceptual change requires small edits across many modules.

    Code quality and technical debt
  • Significance test

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

    Evaluations and benchmarks
  • Simplify conditional

    Simplify conditional 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
  • Simpson's paradox

    Simpson's paradox 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
  • Single point of failure

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

    Reliability and observability
  • Single responsibility principle

    A design principle that asks a software unit to have one coherent reason to change.

    Code quality and technical debt
  • Single-piece flow

    Single-piece flow is a process pattern in which work advances individually or in very small units through the value stream. It aims to expose problems early and reduce the waiting created by large batches.

    Flow and capacity planning
  • Skewness

    Skewness 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
  • Slack time

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

    Flow and capacity planning
  • Slice-based evaluation

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

    Evaluations and benchmarks
  • SLO alerting

    SLO alerting is creation of notifications from measured service objectives and remaining error budget.

    Reliability and observability
  • SLO breach

    SLO breach is a software delivery concept used to describe a specific event, interval, control, or operating condition in the path from source change to production behavior. A useful definition names the boundary, unit, and decision the measure supports.

    DORA and DevOps
  • Small batch

    Small batch 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
  • Small sample size

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

    Engineering analytics
  • Smoke test deployment

    Smoke test deployment is a release engineering and DevOps concept for controlling how software changes are prepared, introduced, or understood.

    DORA and DevOps
  • Smoke testing

    Smoke testing is a brief set of checks that determines whether a build or deployment is stable enough for more detailed testing. It typically exercises a few critical paths and fails fast when a basic capability is unavailable.

    Code quality and technical debt
  • Snapshot metric

    Snapshot metric describes state at one point in time.

    Engineering analytics
  • Soak testing

    Soak 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
  • Software bill of materials

    Software bill of materials 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
  • Software delivery system

    A software delivery system is the connected set of workflows, tools, people, controls, and feedback loops that moves a software change from an idea or request through development and validation into production and operation.

    Engineering analytics
  • Software factory

    A software factory is the connected system an organization uses to design, build, test, review, release, and learn from software. It includes human responsibilities, delivery workflows, platforms, automation, quality controls, and the feedback that improves the system over time.

    Engineering analytics