A reference from Weave

Find a term

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 T

155 terms

  • Timeout budget

    Timeout budget is the serving concept concerned with timeout budget during AI inference.

    Inference performance

  • Timeout policy

    Timeout policy 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

  • Timestamp

    Timestamp anchors ordering, duration, windows, and freshness.

    Engineering analytics

  • Timestamp skew

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

    Engineering analytics

  • Timezone bias

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

    Engineering analytics

  • Toil ratio

    Toil ratio 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

  • Token budget

    A token budget is a configured limit or allowance for the tokens an AI system may process or generate during a request, task, or accounting period. The exact scope can refer to output length, context capacity, spending, or a workflow's total usage.

    Token costs and AI ROI

  • Token budget policy

    Token budget policy 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

  • Token budget utilization

    Token budget utilization is the percentage of a token budget consumed in a period. 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

  • Token budget variance

    Token budget variance is the difference between planned token consumption and actual consumption. 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

  • Token embedding

    Token embedding 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

  • Token forecast

    Token forecast is an estimate of future token consumption from workload history and planned demand. 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

  • Token ledger

    Token ledger is a durable accounting record of token usage and explanatory dimensions. 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

  • Token meter

    Token meter is a mechanism that records token usage for requests or workflows. 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

  • Token overrun

    Token overrun is usage that exceeds a configured allowance or expected request envelope. 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

  • Token quota

    Token quota is a token allowance assigned to a user, team, application, or account. 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

  • Token rate

    Token rate is the number of tokens processed per unit of 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

  • Token streaming

    Token streaming is the serving concept concerned with token streaming during AI inference.

    Inference performance

  • Token utilization

    Token utilization is how much of an available token allowance a workload consumes. 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

  • Token volume

    Token volume is the amount of input and output text processed by an AI workload. 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

  • Tokenization

    Tokenization is the process of converting text into the token units a language model receives and generates. A token can represent a word, part of a word, punctuation, or another piece of text, depending on the tokenizer.

    LLM fundamentals

  • Tokens per second

    Tokens per second is the serving concept concerned with tokens per second during AI inference.

    Inference performance

  • Tool call normalization

    Tool call normalization 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

  • Tool calling

    Tool calling is an interface in which a language model returns a structured request for an application-defined function or external action. The application validates and executes the tool, then supplies the result back to the model.

    AI coding and agents

  • Tool capability routing

    Tool capability routing 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

  • Tool latency

    Tool latency is the time an application or agent waits for a tool call to complete. It includes work such as a database query, API request, file operation, or test run between model responses.

    Inference performance

  • Tool-use evaluation

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

    Evaluations and benchmarks

  • Top-k sampling

    Top-k sampling 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

  • Top-p sampling

    Top-p sampling, also called nucleus sampling, selects the next token from the smallest group whose cumulative probability reaches a chosen probability p. Lower p values restrict the candidate set, while higher values allow more candidates.

    LLM fundamentals

  • Toxicity evaluation

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

    Evaluations and benchmarks

  • Trace context

    Trace context is the small set of identifiers and flags passed between components so telemetry can associate an operation with its trace and parent span. It allows distributed work to be represented as one connected execution.

    Reliability and observability

  • Trace ID

    Trace ID is the identifier shared by spans belonging to one distributed operation.

    Reliability and observability

  • Trace sampling

    Trace sampling is selection of traces or spans to retain from a larger set of observed operations.

    Reliability and observability

  • Trace signal

    Trace signal is evidence that records the path and timing of one operation across services, queues, and databases.

    Reliability and observability

  • Trace waterfall

    Trace waterfall is a time-aligned view showing span nesting, overlap, waiting, and duration.

    Reliability and observability

  • Traffic mirroring

    Traffic mirroring 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

  • Traffic shadowing

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

    DORA and DevOps

  • Train-test contamination

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

    Evaluations and benchmarks

  • Training batch size

    Batch size 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

  • Training data contamination

    Training data contamination is a language-model concept about training behavior and measurement. It names a mechanism, representation, training practice, or operational behavior that can change how an AI system processes input and produces output.

    LLM fundamentals

  • Training loss

    Training loss is a language-model concept about training behavior and measurement. It names a mechanism, representation, training practice, or operational behavior that can change how an AI system processes input and produces output.

    LLM fundamentals

  • Training step

    Training step is a language-model concept about training behavior and measurement. It names a mechanism, representation, training practice, or operational behavior that can change how an AI system processes input and produces output.

    LLM fundamentals

  • Transformer block

    Transformer block 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

  • Transitive dependency

    Transitive dependency 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

  • Treatment effect

    Treatment effect 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

  • Treatment group

    Treatment group 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

  • Treatment spillover

    Treatment spillover 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

  • Tree of thoughts

    Tree of thoughts 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

  • Triage queue

    A triage queue contains requests that have arrived but have not yet been classified or routed into a delivery, incident, support, or rejection path. Its age measures intake delay rather than implementation delay.

    Flow and capacity planning

  • Trunk stability

    Trunk stability 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