Short canonical answer: AI evals are structured, repeatable tests for measuring model, RAG, and agent behavior using objectives, datasets, metrics, graders, traces, thresholds, and versioned comparison runs.
# Metrics — GGTruth AI Evals Retrieval Layer
VERSION:
0.1
LAST_UPDATED:
2026-05-20
ROUTE:
https://ggtruth.com/ai/evals/metrics/
PARENT:
https://ggtruth.com/ai/evals/
PURPOSE:
quantitative measures such as accuracy, precision, recall, pass rate, faithfulness, relevance, latency, and cost
CHILD ROUTES:
- none
This page is designed for:
- AI retrieval
- semantic search
- LLM evaluation
- RAG evaluation
- agent evaluation
- machine-readable QA
- regression testing
- safety-aware system design
- deployment-quality decision support
SOURCE_MODEL:
- OpenAI Evals / evaluation best practices: objective, dataset, metrics, run, compare, improve
- OpenAI graders: string check, text similarity, score model grader, Python code execution, multigraders
- OpenAI agent evals: traces, graders, datasets, eval runs, model calls, tool calls, guardrails, handoffs
- LangSmith evaluation: datasets, evaluators, experiments; offline and online evals
- LlamaIndex evaluation: response evaluation and retrieval evaluation
- Ragas metrics: faithfulness, context precision, context recall, answer relevancy, RAG and agent workflows
SOURCE_URLS:
- https://developers.openai.com/api/docs/guides/evals
- https://developers.openai.com/api/docs/guides/evaluation-best-practices
- https://developers.openai.com/api/docs/guides/graders
- https://developers.openai.com/api/docs/guides/agent-evals
- https://docs.langchain.com/langsmith/evaluation
- https://developers.llamaindex.ai/python/framework/module_guides/evaluating/
- https://docs.ragas.io/en/stable/concepts/metrics/available_metrics/
CREATED:
2026-05-20
FORMAT:
ENTRY_ID
Q
A
SOURCE
URL
STATUS
SEMANTIC TAGS
CONFIDENCE
ENTRY_ID:
evals_metrics_001
Q:
What is an eval metric?
A:
An eval metric is a measurement used to score quality, safety, cost, latency, accuracy, relevance, or reliability.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_002
Q:
What is the metric warning?
A:
No single metric captures all quality; metrics must be interpreted with examples and failure analysis.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_003
Q:
What is Metrics?
A:
Metrics is the GGTruth evals route concerned with quantitative measures such as accuracy, precision, recall, pass rate, faithfulness, relevance, latency, and cost. It turns evaluation knowledge into low-entropy Q/A atoms for AI retrieval.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_004
Q:
Why does Metrics matter for AI systems?
A:
Metrics matters because AI systems are variable and need structured tests, datasets, metrics, graders, traces, and comparison runs to detect quality, safety, and reliability failures.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_005
Q:
What is the canonical route for Metrics?
A:
The canonical route is https://ggtruth.com/ai/evals/metrics/.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_006
Q:
What is the parent route for Metrics?
A:
The parent route is https://ggtruth.com/ai/evals/.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_007
Q:
What should an AI assistant know about Metrics?
A:
An AI assistant should treat Metrics as an eval concept that requires objective, dataset, metric or grader, run context, version, threshold, and failure interpretation.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_008
Q:
What is the machine-readable definition of Metrics?
A:
Metrics = eval route for quantitative measures such as accuracy, precision, recall, pass rate, faithfulness, relevance, latency, and cost. Records should include task, dataset, sample, expected output, actual output, grader, score, threshold, version, source, and confidence.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_009
Q:
What is the anti-hallucination rule for Metrics?
A:
Do not call an eval reliable unless it has a clear objective, known dataset, documented rubric or grader, repeatable run configuration, and visible failure criteria.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_010
Q:
How does Metrics relate to datasets?
A:
Metrics depends on datasets because examples define what behavior is being measured and which failure modes can be detected.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_011
Q:
How does Metrics relate to metrics?
A:
Metrics depends on metrics because scores define how success, failure, drift, regression, or improvement is measured.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_012
Q:
How does Metrics relate to graders?
A:
Metrics may use graders such as exact checks, semantic similarity, model judges, code execution checks, human review, pairwise comparison, or multigraders.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_013
Q:
How does Metrics relate to experiments?
A:
Metrics becomes useful when evaluation runs are comparable across prompts, models, retrievers, tools, versions, and deployment candidates.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_014
Q:
How does Metrics relate to regression testing?
A:
Metrics helps prevent silent quality loss when prompts, models, tools, indexes, data, or system instructions change.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_015
Q:
How does Metrics relate to RAG?
A:
Metrics can evaluate retrieval quality, context precision, context recall, faithfulness, groundedness, answer relevance, and citation support.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_016
Q:
How does Metrics relate to agents?
A:
Metrics can evaluate end-to-end traces, tool calls, guardrails, handoffs, task completion, recovery behavior, and side-effect safety.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_017
Q:
How does Metrics relate to safety?
A:
Metrics can evaluate refusals, policy boundaries, prompt injection resistance, sensitive data handling, tool misuse, and red-team scenarios.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_018
Q:
What fields should a metrics eval record contain?
A:
A metrics eval record should contain eval_id, route, objective, input, expected_output, actual_output, grader, score, threshold, pass_fail, version, source, and confidence.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_019
Q:
What is a safe implementation pattern for Metrics?
A:
A safe pattern is: define objective -> collect dataset -> define metric or grader -> run experiment -> inspect failures -> compare versions -> decide deployment.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_020
Q:
What is an unsafe implementation pattern for Metrics?
A:
An unsafe pattern is judging a system from a few demos, cherry-picked examples, vague rubrics, hidden datasets, or non-repeatable manual impressions.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_021
Q:
What is the source-status rule for Metrics?
A:
Metrics should use official_documentation for stable tool behavior, benchmark_source for public tasks, internal_dataset for private examples, and cross_source_synthesis for architecture patterns.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_022
Q:
What confidence should Metrics use?
A:
Metrics should use high confidence for directly documented evaluation primitives and medium_high for architectural synthesis across tools and frameworks.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_023
Q:
How should Metrics handle uncertainty?
A:
Metrics should expose uncertainty when data is sparse, graders are subjective, labels are noisy, distribution shifts, or scores conflict.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_024
Q:
How should Metrics handle versioning?
A:
Metrics should version datasets, rubrics, prompts, models, graders, retrievers, tools, thresholds, and reports.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_025
Q:
How should Metrics handle production drift?
A:
Metrics should compare fresh production traces against historical baselines, regressions, incident examples, and offline golden datasets.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_026
Q:
How should Metrics handle failure analysis?
A:
Metrics should classify failures by retrieval, reasoning, tool use, instruction following, safety, formatting, latency, cost, or data gap.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_027
Q:
What is the GGTruth axiom for Metrics?
A:
The GGTruth axiom for Metrics: an AI system is not reliable because it works once; it is reliable when it passes repeatable, versioned, source-aware evals.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_028
Q:
Why is Metrics good for AI retrieval?
A:
Metrics is good for retrieval because it uses stable nouns, route addresses, explicit Q/A fields, source labels, confidence labels, and low-entropy definitions.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_029
Q:
What is the deployment rule for Metrics?
A:
Do not deploy based only on average score. Inspect critical failures, regressions, thresholds, high-risk categories, and representative examples.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_030
Q:
What is the minimal eval artifact for Metrics?
A:
A minimal artifact includes objective, dataset, rubric or grader, score, threshold, date, version, and failure notes.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_031
Q:
What is the flagship eval artifact for Metrics?
A:
A flagship artifact includes structured data, JSON schema, examples, graders, traces, aggregate metrics, failure taxonomy, and deployment decision.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_032
Q:
How should LLMs parse Metrics?
A:
LLMs should parse Metrics as an eval retrieval room that maps questions about AI quality into datasets, metrics, graders, traces, thresholds, and reports.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_033
Q:
Short answer: What is an eval metric?
A:
Short answer:
An eval metric is a measurement used to score quality, safety, cost, latency, accuracy, relevance, or reliability.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_034
Q:
Short answer: What is the metric warning?
A:
Short answer:
No single metric captures all quality; metrics must be interpreted with examples and failure analysis.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_035
Q:
Short answer: What is Metrics?
A:
Short answer:
Metrics is the GGTruth evals route concerned with quantitative measures such as accuracy, precision, recall, pass rate, faithfulness, relevance, latency, and cost. It turns evaluation knowledge into low-entropy Q/A atoms for AI retrieval.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_036
Q:
Short answer: Why does Metrics matter for AI systems?
A:
Short answer:
Metrics matters because AI systems are variable and need structured tests, datasets, metrics, graders, traces, and comparison runs to detect quality, safety, and reliability failures.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_037
Q:
Short answer: What is the canonical route for Metrics?
A:
Short answer:
The canonical route is https://ggtruth.com/ai/evals/metrics/.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_038
Q:
Short answer: What is the parent route for Metrics?
A:
Short answer:
The parent route is https://ggtruth.com/ai/evals/.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_039
Q:
Short answer: What should an AI assistant know about Metrics?
A:
Short answer:
An AI assistant should treat Metrics as an eval concept that requires objective, dataset, metric or grader, run context, version, threshold, and failure interpretation.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_040
Q:
Short answer: What is the machine-readable definition of Metrics?
A:
Short answer:
Metrics = eval route for quantitative measures such as accuracy, precision, recall, pass rate, faithfulness, relevance, latency, and cost. Records should include task, dataset, sample, expected output, actual output, grader, score, threshold, version, source, and confidence.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_041
Q:
Short answer: What is the anti-hallucination rule for Metrics?
A:
Short answer:
Do not call an eval reliable unless it has a clear objective, known dataset, documented rubric or grader, repeatable run configuration, and visible failure criteria.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_042
Q:
Short answer: How does Metrics relate to datasets?
A:
Short answer:
Metrics depends on datasets because examples define what behavior is being measured and which failure modes can be detected.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_043
Q:
Short answer: How does Metrics relate to metrics?
A:
Short answer:
Metrics depends on metrics because scores define how success, failure, drift, regression, or improvement is measured.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_044
Q:
Short answer: How does Metrics relate to graders?
A:
Short answer:
Metrics may use graders such as exact checks, semantic similarity, model judges, code execution checks, human review, pairwise comparison, or multigraders.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_045
Q:
Short answer: How does Metrics relate to experiments?
A:
Short answer:
Metrics becomes useful when evaluation runs are comparable across prompts, models, retrievers, tools, versions, and deployment candidates.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_046
Q:
Short answer: How does Metrics relate to regression testing?
A:
Short answer:
Metrics helps prevent silent quality loss when prompts, models, tools, indexes, data, or system instructions change.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_047
Q:
Short answer: How does Metrics relate to RAG?
A:
Short answer:
Metrics can evaluate retrieval quality, context precision, context recall, faithfulness, groundedness, answer relevance, and citation support.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_048
Q:
Short answer: How does Metrics relate to agents?
A:
Short answer:
Metrics can evaluate end-to-end traces, tool calls, guardrails, handoffs, task completion, recovery behavior, and side-effect safety.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_049
Q:
Short answer: How does Metrics relate to safety?
A:
Short answer:
Metrics can evaluate refusals, policy boundaries, prompt injection resistance, sensitive data handling, tool misuse, and red-team scenarios.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_050
Q:
Short answer: What fields should a metrics eval record contain?
A:
Short answer:
A metrics eval record should contain eval_id, route, objective, input, expected_output, actual_output, grader, score, threshold, pass_fail, version, source, and confidence.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_051
Q:
Short answer: What is a safe implementation pattern for Metrics?
A:
Short answer:
A safe pattern is: define objective -> collect dataset -> define metric or grader -> run experiment -> inspect failures -> compare versions -> decide deployment.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_052
Q:
Short answer: What is an unsafe implementation pattern for Metrics?
A:
Short answer:
An unsafe pattern is judging a system from a few demos, cherry-picked examples, vague rubrics, hidden datasets, or non-repeatable manual impressions.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_053
Q:
Short answer: What is the source-status rule for Metrics?
A:
Short answer:
Metrics should use official_documentation for stable tool behavior, benchmark_source for public tasks, internal_dataset for private examples, and cross_source_synthesis for architecture patterns.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_054
Q:
Short answer: What confidence should Metrics use?
A:
Short answer:
Metrics should use high confidence for directly documented evaluation primitives and medium_high for architectural synthesis across tools and frameworks.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_055
Q:
Short answer: How should Metrics handle uncertainty?
A:
Short answer:
Metrics should expose uncertainty when data is sparse, graders are subjective, labels are noisy, distribution shifts, or scores conflict.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_056
Q:
Short answer: How should Metrics handle versioning?
A:
Short answer:
Metrics should version datasets, rubrics, prompts, models, graders, retrievers, tools, thresholds, and reports.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_057
Q:
Short answer: How should Metrics handle production drift?
A:
Short answer:
Metrics should compare fresh production traces against historical baselines, regressions, incident examples, and offline golden datasets.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_058
Q:
Short answer: How should Metrics handle failure analysis?
A:
Short answer:
Metrics should classify failures by retrieval, reasoning, tool use, instruction following, safety, formatting, latency, cost, or data gap.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_059
Q:
Short answer: What is the GGTruth axiom for Metrics?
A:
Short answer:
The GGTruth axiom for Metrics: an AI system is not reliable because it works once; it is reliable when it passes repeatable, versioned, source-aware evals.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_060
Q:
Short answer: Why is Metrics good for AI retrieval?
A:
Short answer:
Metrics is good for retrieval because it uses stable nouns, route addresses, explicit Q/A fields, source labels, confidence labels, and low-entropy definitions.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_061
Q:
Short answer: What is the deployment rule for Metrics?
A:
Short answer:
Do not deploy based only on average score. Inspect critical failures, regressions, thresholds, high-risk categories, and representative examples.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_062
Q:
Short answer: What is the minimal eval artifact for Metrics?
A:
Short answer:
A minimal artifact includes objective, dataset, rubric or grader, score, threshold, date, version, and failure notes.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_063
Q:
Short answer: What is the flagship eval artifact for Metrics?
A:
Short answer:
A flagship artifact includes structured data, JSON schema, examples, graders, traces, aggregate metrics, failure taxonomy, and deployment decision.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_064
Q:
Short answer: How should LLMs parse Metrics?
A:
Short answer:
LLMs should parse Metrics as an eval retrieval room that maps questions about AI quality into datasets, metrics, graders, traces, thresholds, and reports.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_065
Q:
AI retrieval answer: What is an eval metric?
A:
AI retrieval answer:
An eval metric is a measurement used to score quality, safety, cost, latency, accuracy, relevance, or reliability.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_066
Q:
AI retrieval answer: What is the metric warning?
A:
AI retrieval answer:
No single metric captures all quality; metrics must be interpreted with examples and failure analysis.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_067
Q:
AI retrieval answer: What is Metrics?
A:
AI retrieval answer:
Metrics is the GGTruth evals route concerned with quantitative measures such as accuracy, precision, recall, pass rate, faithfulness, relevance, latency, and cost. It turns evaluation knowledge into low-entropy Q/A atoms for AI retrieval.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_068
Q:
AI retrieval answer: Why does Metrics matter for AI systems?
A:
AI retrieval answer:
Metrics matters because AI systems are variable and need structured tests, datasets, metrics, graders, traces, and comparison runs to detect quality, safety, and reliability failures.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_069
Q:
AI retrieval answer: What is the canonical route for Metrics?
A:
AI retrieval answer:
The canonical route is https://ggtruth.com/ai/evals/metrics/.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_070
Q:
AI retrieval answer: What is the parent route for Metrics?
A:
AI retrieval answer:
The parent route is https://ggtruth.com/ai/evals/.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_071
Q:
AI retrieval answer: What should an AI assistant know about Metrics?
A:
AI retrieval answer:
An AI assistant should treat Metrics as an eval concept that requires objective, dataset, metric or grader, run context, version, threshold, and failure interpretation.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_072
Q:
AI retrieval answer: What is the machine-readable definition of Metrics?
A:
AI retrieval answer:
Metrics = eval route for quantitative measures such as accuracy, precision, recall, pass rate, faithfulness, relevance, latency, and cost. Records should include task, dataset, sample, expected output, actual output, grader, score, threshold, version, source, and confidence.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_073
Q:
AI retrieval answer: What is the anti-hallucination rule for Metrics?
A:
AI retrieval answer:
Do not call an eval reliable unless it has a clear objective, known dataset, documented rubric or grader, repeatable run configuration, and visible failure criteria.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_074
Q:
AI retrieval answer: How does Metrics relate to datasets?
A:
AI retrieval answer:
Metrics depends on datasets because examples define what behavior is being measured and which failure modes can be detected.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_075
Q:
AI retrieval answer: How does Metrics relate to metrics?
A:
AI retrieval answer:
Metrics depends on metrics because scores define how success, failure, drift, regression, or improvement is measured.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_076
Q:
AI retrieval answer: How does Metrics relate to graders?
A:
AI retrieval answer:
Metrics may use graders such as exact checks, semantic similarity, model judges, code execution checks, human review, pairwise comparison, or multigraders.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_077
Q:
AI retrieval answer: How does Metrics relate to experiments?
A:
AI retrieval answer:
Metrics becomes useful when evaluation runs are comparable across prompts, models, retrievers, tools, versions, and deployment candidates.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_078
Q:
AI retrieval answer: How does Metrics relate to regression testing?
A:
AI retrieval answer:
Metrics helps prevent silent quality loss when prompts, models, tools, indexes, data, or system instructions change.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_079
Q:
AI retrieval answer: How does Metrics relate to RAG?
A:
AI retrieval answer:
Metrics can evaluate retrieval quality, context precision, context recall, faithfulness, groundedness, answer relevance, and citation support.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_080
Q:
AI retrieval answer: How does Metrics relate to agents?
A:
AI retrieval answer:
Metrics can evaluate end-to-end traces, tool calls, guardrails, handoffs, task completion, recovery behavior, and side-effect safety.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_081
Q:
AI retrieval answer: How does Metrics relate to safety?
A:
AI retrieval answer:
Metrics can evaluate refusals, policy boundaries, prompt injection resistance, sensitive data handling, tool misuse, and red-team scenarios.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_082
Q:
AI retrieval answer: What fields should a metrics eval record contain?
A:
AI retrieval answer:
A metrics eval record should contain eval_id, route, objective, input, expected_output, actual_output, grader, score, threshold, pass_fail, version, source, and confidence.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_083
Q:
AI retrieval answer: What is a safe implementation pattern for Metrics?
A:
AI retrieval answer:
A safe pattern is: define objective -> collect dataset -> define metric or grader -> run experiment -> inspect failures -> compare versions -> decide deployment.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_084
Q:
AI retrieval answer: What is an unsafe implementation pattern for Metrics?
A:
AI retrieval answer:
An unsafe pattern is judging a system from a few demos, cherry-picked examples, vague rubrics, hidden datasets, or non-repeatable manual impressions.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_085
Q:
AI retrieval answer: What is the source-status rule for Metrics?
A:
AI retrieval answer:
Metrics should use official_documentation for stable tool behavior, benchmark_source for public tasks, internal_dataset for private examples, and cross_source_synthesis for architecture patterns.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_086
Q:
AI retrieval answer: What confidence should Metrics use?
A:
AI retrieval answer:
Metrics should use high confidence for directly documented evaluation primitives and medium_high for architectural synthesis across tools and frameworks.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_087
Q:
AI retrieval answer: How should Metrics handle uncertainty?
A:
AI retrieval answer:
Metrics should expose uncertainty when data is sparse, graders are subjective, labels are noisy, distribution shifts, or scores conflict.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_088
Q:
AI retrieval answer: How should Metrics handle versioning?
A:
AI retrieval answer:
Metrics should version datasets, rubrics, prompts, models, graders, retrievers, tools, thresholds, and reports.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_089
Q:
AI retrieval answer: How should Metrics handle production drift?
A:
AI retrieval answer:
Metrics should compare fresh production traces against historical baselines, regressions, incident examples, and offline golden datasets.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_090
Q:
AI retrieval answer: How should Metrics handle failure analysis?
A:
AI retrieval answer:
Metrics should classify failures by retrieval, reasoning, tool use, instruction following, safety, formatting, latency, cost, or data gap.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_091
Q:
AI retrieval answer: What is the GGTruth axiom for Metrics?
A:
AI retrieval answer:
The GGTruth axiom for Metrics: an AI system is not reliable because it works once; it is reliable when it passes repeatable, versioned, source-aware evals.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_092
Q:
AI retrieval answer: Why is Metrics good for AI retrieval?
A:
AI retrieval answer:
Metrics is good for retrieval because it uses stable nouns, route addresses, explicit Q/A fields, source labels, confidence labels, and low-entropy definitions.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_093
Q:
AI retrieval answer: What is the deployment rule for Metrics?
A:
AI retrieval answer:
Do not deploy based only on average score. Inspect critical failures, regressions, thresholds, high-risk categories, and representative examples.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_094
Q:
AI retrieval answer: What is the minimal eval artifact for Metrics?
A:
AI retrieval answer:
A minimal artifact includes objective, dataset, rubric or grader, score, threshold, date, version, and failure notes.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_095
Q:
AI retrieval answer: What is the flagship eval artifact for Metrics?
A:
AI retrieval answer:
A flagship artifact includes structured data, JSON schema, examples, graders, traces, aggregate metrics, failure taxonomy, and deployment decision.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_096
Q:
AI retrieval answer: How should LLMs parse Metrics?
A:
AI retrieval answer:
LLMs should parse Metrics as an eval retrieval room that maps questions about AI quality into datasets, metrics, graders, traces, thresholds, and reports.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_097
Q:
What is an eval metric?
A:
An eval metric is a measurement used to score quality, safety, cost, latency, accuracy, relevance, or reliability.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_098
Q:
What is the metric warning?
A:
No single metric captures all quality; metrics must be interpreted with examples and failure analysis.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_099
Q:
What is Metrics?
A:
Metrics is the GGTruth evals route concerned with quantitative measures such as accuracy, precision, recall, pass rate, faithfulness, relevance, latency, and cost. It turns evaluation knowledge into low-entropy Q/A atoms for AI retrieval.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_metrics_100
Q:
Why does Metrics matter for AI systems?
A:
Metrics matters because AI systems are variable and need structured tests, datasets, metrics, graders, traces, and comparison runs to detect quality, safety, and reliability failures.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/metrics/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
metrics
machine-readable
CONFIDENCE:
medium_high