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

  • Metric Trend

    Metric Trend is an analytical concept that helps describe, summarize, or interpret engineering evidence under a stated measurement design.

    Engineering analytics

  • Metric validity

    Metric 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

  • Metric Variance

    Metric Variance is an analytical concept that helps describe, summarize, or interpret engineering evidence under a stated measurement design.

    Engineering analytics

  • Metric versioning

    Metric versioning why metric definitions need explicit versions.

    Engineering analytics

  • Metric Volatility

    Metric Volatility is an analytical concept that helps describe, summarize, or interpret engineering evidence under a stated measurement design.

    Engineering analytics

  • Metric Window

    Metric Window is an analytical concept that helps describe, summarize, or interpret engineering evidence under a stated measurement design.

    Engineering analytics

  • Microbatching

    Microbatching is the serving concept concerned with microbatching during AI inference.

    Inference performance

  • Migration plan

    Migration plan 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

  • Milestone

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

    Flow and capacity planning

  • Milestone risk

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

    Flow and capacity planning

  • Minimum generation length

    Minimum generation length 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

  • Missing at random

    Missing at random 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

  • Missing completely at random

    Missing completely at random 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

  • Missing data

    Missing data describes absent values or records that affect an analysis.

    Engineering analytics

  • Missing not at random

    Missing not at random 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

  • Mixed precision inference

    Mixed precision inference is the serving concept concerned with mixed precision inference during AI inference.

    Inference performance

  • Mixed precision training

    Mixed precision training 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 fundamentals

  • Mob programming

    Mob programming 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

  • Modality routing

    Modality 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

  • Mode

    Mode 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

  • Model allowlist

    Model allowlist 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

  • Model capability matrix

    Model capability matrix 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

  • Model Context Protocol

    Model Context Protocol, or MCP, is an open protocol for connecting AI applications with external tools, resources, and prompts through a standardized interface. It gives clients and servers a common way to describe and invoke capabilities.

    AI coding and agents

  • Model denylist

    Model denylist 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

  • Model fallback

    Model fallback is the use of an alternate model or provider when the preferred route cannot handle a request or fails a defined condition. A fallback policy can respond to outages, rate limits, unsupported capabilities, timeouts, or application-level checks.

    Model routing and gateways

  • Model fallback cost

    Model fallback cost is extra expense when a primary model fails and another route serves work. 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

  • Model gateway

    A model gateway is a service layer that gives applications a common interface to one or more model providers. Depending on its design, it can handle routing, authentication, retries, fallbacks, usage tracking, and request policies.

    Model routing and gateways

  • Model loading

    Model loading is the serving concept concerned with model loading during AI inference.

    Inference performance

  • Model mix

    Model mix is the distribution of workload across models, providers, or deployment tiers. 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

  • Model parallelism

    Model parallelism is the serving concept concerned with model parallelism during AI inference.

    Inference performance

  • Model registry

    Model registry 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

  • Model replica

    Model replica is the serving concept concerned with model replica during AI inference.

    Inference performance

  • Model replication

    Model replication is the serving concept concerned with model replication during AI inference.

    Inference performance

  • Model routing

    Model routing is the process of selecting which AI model handles a request or a step in a workflow. A routing policy can consider the task, required capabilities, expected quality, price, latency, and provider availability.

    Model routing and gateways

  • Model routing savings

    Model routing savings is spend reduction from sending work to a suitable efficient route. 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

  • Model version pinning

    Model version pinning 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

  • Model warmup

    Model warmup is the serving concept concerned with model warmup during AI inference.

    Inference performance

  • Model-based testing

    Model-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

  • Moderator variable

    Moderator variable 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

  • Modular monolith

    Modular monolith 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

  • Module cohesion

    The degree to which the elements of a module support one focused purpose.

    Code quality and technical debt

  • Module stability

    The degree to which a module can change without forcing changes in its consumers.

    Code quality and technical debt

  • Module testing

    Module 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

  • Monolith decomposition

    Monolith decomposition 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

  • Monte Carlo simulation

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

    Flow and capacity planning

  • Monthly recurring AI spend

    Monthly recurring AI spend is the recurring portion of monthly AI expense for ongoing workloads. 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

  • Moving average

    Moving average 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

  • Multi Agent Orchestration

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

    AI coding and agents

  • Multi-query attention

    Multi-query attention is a language-model concept about evaluation design and failure analysis. It names a mechanism, representation, training practice, or operational behavior that can change how an AI system processes input and produces output.

    LLM fundamentals

  • Multi-region routing

    Multi-region 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