Evaluation and reliability

A/B testing

A randomized controlled experiment that exposes comparable groups to different variants and compares a predefined outcome.

Ablation

An experiment that removes or changes one component while holding others as constant as practical to estimate that component's contribution.

Abstention

An explicit outcome in which a model or evaluator declines to answer, act, or judge because evidence or authority is insufficient.

Acceptance criteria

Explicit conditions an outcome must satisfy before it can be accepted, promoted, or declared complete.

Accuracy

The proportion of evaluated predictions counted as correct under a stated label set, unit of analysis, and decision rule.

Benchmark

A defined package of tasks, data, metrics, and execution rules used to compare systems under the same evaluation regime.

Calibration

The degree to which a system's stated probabilities match observed outcome frequencies across comparable predictions.

Concept drift

A change over time in the relationship between inputs and the correct target or decision.

Confusion matrix

A table counting predicted classes against actual classes, including true and false positives and negatives.

Cross-validation

A resampling method that estimates generalization by repeatedly training and evaluating on different non-overlapping subsets of available data.

Data drift

A change over time in the distribution of system inputs or features.

Evaluation (eval)

A systematic measurement of model or system behavior against defined tasks, criteria, datasets, or operational outcomes.

F1 score

The harmonic mean of precision and recall.

False negative

An outcome incorrectly classified as absent when it is actually present.

False positive

A negative case that a classifier or detector incorrectly labels as positive.

Golden set

A curated set of reference examples with reviewed expected outcomes used for evaluation or regression testing.

Grader

A deterministic check, model, or human protocol that judges an output or trajectory against stated criteria.

Held-out set

Examples kept separate from training and development decisions so evaluation measures generalization to data the workflow has not optimized against.

Independent verification

Checking an outcome with evidence, components, context, or authorities meaningfully separated from the system that produced it.

LLM as judge

Using a language model to evaluate, compare, classify, or score outputs produced by models or agents.

Nondeterminism

The property that identical-looking requests can produce different behavior because of sampling, concurrency, infrastructure, model updates, or hidden state.

Observability

The ability to infer a system's internal state and behavior from emitted traces, logs, metrics, events, and artifacts.

Oracle

A mechanism that can determine the expected or acceptable result for a task, such as a compiler, formal specification, invariant, test suite, or reference implementation.

Precision

Among predicted-positive cases, the proportion that are truly positive.

Recall

Among truly positive cases, the proportion correctly identified as positive.

Reward hacking

Achieving a high measured reward through behavior that exploits the metric or evaluator without accomplishing the intended objective.

Specification gaming

Satisfying the literal specification or metric in a way that violates its intended purpose.

Sycophancy

A failure mode in which a model favors agreement with a user's stated belief or preference over an independently supported answer.

Trace

A captured sequence of model calls, tool calls, events, timings, state changes, and outputs from an execution.

Verification gate

A control point that blocks promotion until required evidence has been produced and validated.

Verification loop

A repeated execute, observe, compare, and correct cycle that withholds completion until an attempted result satisfies explicit evidence or acceptance criteria.

Emergent behavior

A capability or behavior that appears qualitatively new at a larger scale or higher level of system interaction rather than as an obvious continuation of smaller-scale measurements.

Model drift

A broad operational term for model behavior or performance changing relative to an accepted baseline.