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 L

40 terms

  • Lack of cohesion

    A family of measures that estimates how weakly the responsibilities or data uses within a class are connected.

    Code quality and technical debt

  • Language-model objective

    Language-model objective 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

  • Late-arriving data

    Late-arriving data describes records delivered after their event window.

    Engineering analytics

  • Latency breakdown

    Latency breakdown is the serving concept concerned with latency breakdown during AI inference.

    Inference performance

  • Latency budget

    Latency budget is the serving concept concerned with latency budget during AI inference.

    Inference performance

  • Latency evaluation

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

    Evaluations and benchmarks

  • Latency-adjusted AI cost

    Latency-adjusted AI cost is a cost comparison that accounts for the operational impact of response time. 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

  • Latent representation

    Latent representation 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

  • Law of Demeter

    A guideline that limits how many unrelated object relationships a method navigates directly.

    Code quality and technical debt

  • Law of large numbers

    Law of large numbers 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

  • Layer normalization

    Layer normalization 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

  • Lead time

    Lead time is the elapsed time between a request entering a workflow and the requested outcome being delivered. In software teams, the start may be prioritization or commitment and the finish may be deployment, so the definition must be stated explicitly.

    Flow and capacity planning

  • Lead-time distribution

    A lead-time distribution shows how elapsed time varies from the chosen demand boundary to the chosen completion boundary. It describes the range and frequency of outcomes rather than one representative average.

    Flow and capacity planning

  • Leader election

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

    Reliability and observability

  • Learning rate schedule

    Learning rate schedule 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

  • Least-loaded routing

    Least-loaded 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

  • Legacy modernization

    Legacy modernization 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

  • Legacy system

    Legacy system 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

  • Length penalty

    Length penalty 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

  • Lines of code

    Lines of code is a count of source lines in a file, change, or codebase. Depending on the tool, the count may include blank lines, comments, generated files, or only executable statements, so the definition must be stated.

    Developer productivity

  • Liskov substitution principle

    A design principle that requires a subtype to remain valid wherever its declared base type is expected.

    Code quality and technical debt

  • Listwise deletion

    Listwise deletion 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

  • Listwise ranking

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

    Evaluations and benchmarks

  • Liveness probe

    Liveness probe is a test of whether a process should be restarted because it no longer functions at a basic level.

    Reliability and observability

  • LLM inference

    LLM inference is the process of running a trained language model on an input to produce an output. For a text-generating model, it typically involves processing the input context and generating additional tokens according to a decoding strategy.

    LLM fundamentals

  • LLM latency

    LLM latency is the time associated with receiving a language model response. Common measures include time to first token, time between generated tokens, and time to the final token, each describing a different user experience.

    Inference performance

  • Load shedding

    Load shedding 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

  • Load testing

    Load 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

  • Localization testing

    Localization 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

  • Log enrichment

    Log enrichment is adding context to a log record such as service identity, deployment, or correlation fields.

    Reliability and observability

  • Log level

    Log level is a classification such as debug, info, warning, or error indicating intended operational importance.

    Reliability and observability

  • Log normal distribution

    Log normal distribution 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

  • Log parsing

    Log parsing is conversion of raw log text or fields into a structured searchable representation.

    Reliability and observability

  • Log retention

    Log retention is the policy determining how long logs remain available and under what archive or deletion rules.

    Reliability and observability

  • Log signal

    Log signal is a timestamped record of an event or state transition emitted by software or infrastructure.

    Reliability and observability

  • Log-based alert

    Log-based alert is a trigger based on matching log patterns, counts, rates, or structured conditions.

    Reliability and observability

  • Logit lens

    Logit lens 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

  • Long context window

    Long context window is the serving concept concerned with long context window during AI inference.

    Inference performance

  • Long-context evaluation

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

    Evaluations and benchmarks

  • Longitudinal study

    Longitudinal study 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