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

  • Product-engineering collaboration

    Product-engineering collaboration 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

  • Production change volume

    Production change volume 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

  • Production evaluation

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

    Evaluations and benchmarks

  • Production readiness

    Production readiness 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

  • Production release event

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

    DORA and DevOps

  • Productivity baseline

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

    Developer productivity

  • Productivity dimensions

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

    Developer productivity

  • Productivity measurement framework

    Productivity measurement framework is a developer productivity concept that helps teams understand productivity measurement framework in the context of software delivery.

    Developer productivity

  • Productivity trend

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

    Developer productivity

  • Productivity variance

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

    Developer productivity

  • Progressive delivery

    Progressive delivery 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

  • Prompt cache savings

    Prompt cache savings is cost avoided when repeated context is served from an eligible cache. 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

  • Prompt caching

    Prompt caching reuses processing associated with previously supplied prompt content, often a matching prefix, to reduce repeated input work. It differs from response caching, which returns a stored answer instead of generating a new one.

    Inference performance

  • Prompt compilation

    Prompt compilation 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

  • Prompt compression

    Prompt compression 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

  • Prompt engineering

    Prompt engineering is the practice of designing and testing instructions, examples, context, and output requirements that guide a language model toward a useful result. It is an iterative engineering activity supported by evaluation and observability.

    LLM fundamentals

  • Prompt injection

    Prompt injection is an attack or unintended instruction that causes a language model to treat untrusted content as higher-priority guidance. It can redirect an application, expose data, or trigger tools in ways the developer did not intend.

    LLM fundamentals

  • Prompt injection defense

    Prompt injection defense 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

  • Prompt length

    Prompt length is the serving concept concerned with prompt length during AI inference.

    Inference performance

  • Prompt overhead

    Prompt overhead is request context supporting execution rather than the primary user payload. 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

  • Prompt registry

    Prompt registry 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

  • Prompt robustness

    Prompt robustness 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

  • Prompt sensitivity

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

    Evaluations and benchmarks

  • Prompt template

    Prompt template 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

  • Prompt variable

    Prompt variable 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

  • Prompt version

    Prompt version 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

  • Propensity score

    Propensity score 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

  • Propensity score matching

    Propensity score matching 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

  • Provider adapter

    Provider adapter 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

  • Provider allowlist

    Provider 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

  • Provider availability

    Provider availability 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

  • Provider capability matrix

    Provider 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

  • Provider denylist

    Provider 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

  • Provider health check

    Provider health check 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

  • Provider health score

    Provider health score 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

  • Provider metrics

    Provider metrics 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

  • Provider quota

    Provider quota 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

  • Provider rate limit

    Provider rate limit 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

  • Provider registry

    Provider 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

  • Provider routing

    Provider routing selects an infrastructure provider or endpoint for a model request. The policy may consider price, latency, availability, regional requirements, quotas, capabilities, or recent provider performance.

    Model routing and gateways

  • Provider SLA

    Provider SLA 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

  • Provider trace

    Provider trace 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

  • Provider version pinning

    Provider 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

  • Provider-agnostic API

    Provider-agnostic API 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

  • Provider-agnostic failover

    Provider-agnostic failover 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

  • Proximal policy optimization

    Proximal policy optimization 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

  • Proxy measure

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

    Engineering analytics

  • Proxy metric

    Proxy metric 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

  • Psychological safety

    Psychological safety 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

  • Pull criteria

    Pull criteria are explicit conditions used to decide whether work is ready for the next stage. They protect flow by preventing items from entering a step when required information, capacity, or quality conditions are missing.

    Flow and capacity planning