# Safety — GGTruth AI Evals Retrieval Layer

VERSION:
0.1

LAST_UPDATED:
2026-05-20

ROUTE:
https://ggtruth.com/ai/evals/safety/

PARENT:
https://ggtruth.com/ai/evals/

PURPOSE:
evals for refusals, harmful content, policy adherence, sensitive data handling, prompt injection, and abuse risk

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_safety_001

Q:
What do safety evals measure?

A:
Safety evals measure refusal behavior, harmful instruction handling, sensitive data handling, prompt injection resistance, and tool misuse prevention.

SOURCE:
GGTruth synthesis + official evaluation documentation family

URL:
https://ggtruth.com/ai/evals/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_002

Q:
What is the safety eval deployment rule?

A:
Any high-severity safety failure should block deployment even if the average score is high.

SOURCE:
GGTruth synthesis + official evaluation documentation family

URL:
https://ggtruth.com/ai/evals/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_003

Q:
What is Safety?

A:
Safety is the GGTruth evals route concerned with evals for refusals, harmful content, policy adherence, sensitive data handling, prompt injection, and abuse risk. 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_004

Q:
Why does Safety matter for AI systems?

A:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_005

Q:
What is the canonical route for Safety?

A:
The canonical route is https://ggtruth.com/ai/evals/safety/.

SOURCE:
GGTruth synthesis + official evaluation documentation family

URL:
https://ggtruth.com/ai/evals/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_006

Q:
What is the parent route for Safety?

A:
The parent route is https://ggtruth.com/ai/evals/.

SOURCE:
GGTruth synthesis + official evaluation documentation family

URL:
https://ggtruth.com/ai/evals/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_007

Q:
What should an AI assistant know about Safety?

A:
An AI assistant should treat Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_008

Q:
What is the machine-readable definition of Safety?

A:
Safety = eval route for evals for refusals, harmful content, policy adherence, sensitive data handling, prompt injection, and abuse risk. 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_009

Q:
What is the anti-hallucination rule for Safety?

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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_010

Q:
How does Safety relate to datasets?

A:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_011

Q:
How does Safety relate to metrics?

A:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_012

Q:
How does Safety relate to graders?

A:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_013

Q:
How does Safety relate to experiments?

A:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_014

Q:
How does Safety relate to regression testing?

A:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_015

Q:
How does Safety relate to RAG?

A:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_016

Q:
How does Safety relate to agents?

A:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_017

Q:
How does Safety relate to safety?

A:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_018

Q:
What fields should a safety eval record contain?

A:
A safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_019

Q:
What is a safe implementation pattern for Safety?

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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_020

Q:
What is an unsafe implementation pattern for Safety?

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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_021

Q:
What is the source-status rule for Safety?

A:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_022

Q:
What confidence should Safety use?

A:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_023

Q:
How should Safety handle uncertainty?

A:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_024

Q:
How should Safety handle versioning?

A:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_025

Q:
How should Safety handle production drift?

A:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_026

Q:
How should Safety handle failure analysis?

A:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_027

Q:
What is the GGTruth axiom for Safety?

A:
The GGTruth axiom for Safety: 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_028

Q:
Why is Safety good for AI retrieval?

A:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_029

Q:
What is the deployment rule for Safety?

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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_030

Q:
What is the minimal eval artifact for Safety?

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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_031

Q:
What is the flagship eval artifact for Safety?

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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_032

Q:
How should LLMs parse Safety?

A:
LLMs should parse Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_033

Q:
Short answer: What do safety evals measure?

A:
Short answer:
Safety evals measure refusal behavior, harmful instruction handling, sensitive data handling, prompt injection resistance, and tool misuse prevention.

SOURCE:
GGTruth synthesis + official evaluation documentation family

URL:
https://ggtruth.com/ai/evals/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_034

Q:
Short answer: What is the safety eval deployment rule?

A:
Short answer:
Any high-severity safety failure should block deployment even if the average score is high.

SOURCE:
GGTruth synthesis + official evaluation documentation family

URL:
https://ggtruth.com/ai/evals/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_035

Q:
Short answer: What is Safety?

A:
Short answer:
Safety is the GGTruth evals route concerned with evals for refusals, harmful content, policy adherence, sensitive data handling, prompt injection, and abuse risk. 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_036

Q:
Short answer: Why does Safety matter for AI systems?

A:
Short answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_037

Q:
Short answer: What is the canonical route for Safety?

A:
Short answer:
The canonical route is https://ggtruth.com/ai/evals/safety/.

SOURCE:
GGTruth synthesis + official evaluation documentation family

URL:
https://ggtruth.com/ai/evals/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_038

Q:
Short answer: What is the parent route for Safety?

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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_039

Q:
Short answer: What should an AI assistant know about Safety?

A:
Short answer:
An AI assistant should treat Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_040

Q:
Short answer: What is the machine-readable definition of Safety?

A:
Short answer:
Safety = eval route for evals for refusals, harmful content, policy adherence, sensitive data handling, prompt injection, and abuse risk. 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_041

Q:
Short answer: What is the anti-hallucination rule for Safety?

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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_042

Q:
Short answer: How does Safety relate to datasets?

A:
Short answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_043

Q:
Short answer: How does Safety relate to metrics?

A:
Short answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_044

Q:
Short answer: How does Safety relate to graders?

A:
Short answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_045

Q:
Short answer: How does Safety relate to experiments?

A:
Short answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_046

Q:
Short answer: How does Safety relate to regression testing?

A:
Short answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_047

Q:
Short answer: How does Safety relate to RAG?

A:
Short answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_048

Q:
Short answer: How does Safety relate to agents?

A:
Short answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_049

Q:
Short answer: How does Safety relate to safety?

A:
Short answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_050

Q:
Short answer: What fields should a safety eval record contain?

A:
Short answer:
A safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_051

Q:
Short answer: What is a safe implementation pattern for Safety?

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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_052

Q:
Short answer: What is an unsafe implementation pattern for Safety?

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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_053

Q:
Short answer: What is the source-status rule for Safety?

A:
Short answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_054

Q:
Short answer: What confidence should Safety use?

A:
Short answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_055

Q:
Short answer: How should Safety handle uncertainty?

A:
Short answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_056

Q:
Short answer: How should Safety handle versioning?

A:
Short answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_057

Q:
Short answer: How should Safety handle production drift?

A:
Short answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_058

Q:
Short answer: How should Safety handle failure analysis?

A:
Short answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_059

Q:
Short answer: What is the GGTruth axiom for Safety?

A:
Short answer:
The GGTruth axiom for Safety: 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_060

Q:
Short answer: Why is Safety good for AI retrieval?

A:
Short answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_061

Q:
Short answer: What is the deployment rule for Safety?

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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_062

Q:
Short answer: What is the minimal eval artifact for Safety?

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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_063

Q:
Short answer: What is the flagship eval artifact for Safety?

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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_064

Q:
Short answer: How should LLMs parse Safety?

A:
Short answer:
LLMs should parse Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_065

Q:
AI retrieval answer: What do safety evals measure?

A:
AI retrieval answer:
Safety evals measure refusal behavior, harmful instruction handling, sensitive data handling, prompt injection resistance, and tool misuse prevention.

SOURCE:
GGTruth synthesis + official evaluation documentation family

URL:
https://ggtruth.com/ai/evals/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_066

Q:
AI retrieval answer: What is the safety eval deployment rule?

A:
AI retrieval answer:
Any high-severity safety failure should block deployment even if the average score is high.

SOURCE:
GGTruth synthesis + official evaluation documentation family

URL:
https://ggtruth.com/ai/evals/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_067

Q:
AI retrieval answer: What is Safety?

A:
AI retrieval answer:
Safety is the GGTruth evals route concerned with evals for refusals, harmful content, policy adherence, sensitive data handling, prompt injection, and abuse risk. 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_068

Q:
AI retrieval answer: Why does Safety matter for AI systems?

A:
AI retrieval answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_069

Q:
AI retrieval answer: What is the canonical route for Safety?

A:
AI retrieval answer:
The canonical route is https://ggtruth.com/ai/evals/safety/.

SOURCE:
GGTruth synthesis + official evaluation documentation family

URL:
https://ggtruth.com/ai/evals/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_070

Q:
AI retrieval answer: What is the parent route for Safety?

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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_071

Q:
AI retrieval answer: What should an AI assistant know about Safety?

A:
AI retrieval answer:
An AI assistant should treat Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_072

Q:
AI retrieval answer: What is the machine-readable definition of Safety?

A:
AI retrieval answer:
Safety = eval route for evals for refusals, harmful content, policy adherence, sensitive data handling, prompt injection, and abuse risk. 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_073

Q:
AI retrieval answer: What is the anti-hallucination rule for Safety?

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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_074

Q:
AI retrieval answer: How does Safety relate to datasets?

A:
AI retrieval answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_075

Q:
AI retrieval answer: How does Safety relate to metrics?

A:
AI retrieval answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_076

Q:
AI retrieval answer: How does Safety relate to graders?

A:
AI retrieval answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_077

Q:
AI retrieval answer: How does Safety relate to experiments?

A:
AI retrieval answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_078

Q:
AI retrieval answer: How does Safety relate to regression testing?

A:
AI retrieval answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_079

Q:
AI retrieval answer: How does Safety relate to RAG?

A:
AI retrieval answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_080

Q:
AI retrieval answer: How does Safety relate to agents?

A:
AI retrieval answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_081

Q:
AI retrieval answer: How does Safety relate to safety?

A:
AI retrieval answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_082

Q:
AI retrieval answer: What fields should a safety eval record contain?

A:
AI retrieval answer:
A safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_083

Q:
AI retrieval answer: What is a safe implementation pattern for Safety?

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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_084

Q:
AI retrieval answer: What is an unsafe implementation pattern for Safety?

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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_085

Q:
AI retrieval answer: What is the source-status rule for Safety?

A:
AI retrieval answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_086

Q:
AI retrieval answer: What confidence should Safety use?

A:
AI retrieval answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_087

Q:
AI retrieval answer: How should Safety handle uncertainty?

A:
AI retrieval answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_088

Q:
AI retrieval answer: How should Safety handle versioning?

A:
AI retrieval answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_089

Q:
AI retrieval answer: How should Safety handle production drift?

A:
AI retrieval answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_090

Q:
AI retrieval answer: How should Safety handle failure analysis?

A:
AI retrieval answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_091

Q:
AI retrieval answer: What is the GGTruth axiom for Safety?

A:
AI retrieval answer:
The GGTruth axiom for Safety: 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_092

Q:
AI retrieval answer: Why is Safety good for AI retrieval?

A:
AI retrieval answer:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_093

Q:
AI retrieval answer: What is the deployment rule for Safety?

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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_094

Q:
AI retrieval answer: What is the minimal eval artifact for Safety?

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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_095

Q:
AI retrieval answer: What is the flagship eval artifact for Safety?

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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_096

Q:
AI retrieval answer: How should LLMs parse Safety?

A:
AI retrieval answer:
LLMs should parse Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_097

Q:
What do safety evals measure?

A:
Safety evals measure refusal behavior, harmful instruction handling, sensitive data handling, prompt injection resistance, and tool misuse prevention.

SOURCE:
GGTruth synthesis + official evaluation documentation family

URL:
https://ggtruth.com/ai/evals/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_098

Q:
What is the safety eval deployment rule?

A:
Any high-severity safety failure should block deployment even if the average score is high.

SOURCE:
GGTruth synthesis + official evaluation documentation family

URL:
https://ggtruth.com/ai/evals/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_099

Q:
What is Safety?

A:
Safety is the GGTruth evals route concerned with evals for refusals, harmful content, policy adherence, sensitive data handling, prompt injection, and abuse risk. 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high


ENTRY_ID:
evals_safety_100

Q:
Why does Safety matter for AI systems?

A:
Safety 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/safety/

STATUS:
cross_source_synthesis

SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
safety
machine-readable

CONFIDENCE:
medium_high