A reference from Weave

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Understand the metrics, models, and methods behind modern engineering. Clear definitions, practical examples, and a closer look at what the numbers actually mean.

Terms beginning with P

140 terms

  • Policy simulation

    Policy simulation 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

  • Policy validation as code

    Policy validation as code is a release engineering and DevOps concept for controlling how software changes are prepared, introduced, or understood.

    DORA and DevOps

  • Policy-based routing

    Policy-based routing 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

  • Portability debt

    Portability debt 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

  • Portability testing

    Portability 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

  • Portfolio capacity

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

    Flow and capacity planning

  • Position bias

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

    Evaluations and benchmarks

  • Position embedding

    Position embedding 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

  • Position interpolation

    Position interpolation 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

  • Post-deployment monitoring

    Post-deployment monitoring 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

  • Postmortem

    A postmortem is a written review of an incident that records what happened, how systems and people responded, what the impact was, and which improvements should follow. A blameless postmortem focuses on system conditions rather than individual fault.

    Reliability and observability

  • Potential outcomes

    Potential outcomes 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

  • Power analysis

    Power 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

  • Practical significance

    Practical significance 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

  • Pre-review checklist

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

    Code review

  • Precision

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

    Evaluations and benchmarks

  • Preference model

    Preference model 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

  • Preference rate

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

    Evaluations and benchmarks

  • Prefill decode disaggregation

    Prefill decode disaggregation is the serving concept concerned with prefill decode disaggregation during AI inference.

    Inference performance

  • Prefill phase

    Prefill phase is the serving concept concerned with prefill phase during AI inference.

    Inference performance

  • Prefix cache

    Prefix cache is the serving concept concerned with prefix cache during AI inference.

    Inference performance

  • Prefix-constrained decoding

    Prefix-constrained decoding 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

  • Preregistration

    Preregistration 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

  • Preview environment

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

    DORA and DevOps

  • Primitive obsession

    A code smell in which domain concepts are represented by generic primitives instead of meaningful types.

    Code quality and technical debt

  • Prioritization

    Prioritization 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

  • Prioritization framework

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

    Flow and capacity planning

  • Priority class

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

    Flow and capacity planning

  • Priority queue

    Priority queue 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

  • Priority routing

    Priority routing 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

  • Privacy evaluation

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

    Evaluations and benchmarks

  • Probabilistic delivery date

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

    Flow and capacity planning

  • Process debt

    Process debt 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

  • Processing time

    Processing time is the serving concept concerned with processing time during AI inference.

    Inference performance

  • Product backlog

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

    Flow and capacity planning

  • Product discovery

    Product discovery 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

  • 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