# Graders — GGTruth AI Evals Retrieval Layer

VERSION:
0.1

LAST_UPDATED:
2026-05-20

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

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

PURPOSE:
scoring components such as string checks, semantic similarity, model graders, code graders, and multigraders

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_graders_001

Q:
What grader types are common?

A:
Common grader types include string checks, text similarity, model-based scoring, code execution checks, pairwise preference, human review, and multigraders.

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_002

Q:
What should graders output?

A:
Graders should output score, reason, pass/fail, confidence, failure category, and trace or example identifier.

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_003

Q:
What is Graders?

A:
Graders is the GGTruth evals route concerned with scoring components such as string checks, semantic similarity, model graders, code graders, and multigraders. 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_004

Q:
Why does Graders matter for AI systems?

A:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_005

Q:
What is the canonical route for Graders?

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

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_006

Q:
What is the parent route for Graders?

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

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_007

Q:
What should an AI assistant know about Graders?

A:
An AI assistant should treat Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_008

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

A:
Graders = eval route for scoring components such as string checks, semantic similarity, model graders, code graders, and multigraders. 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_009

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

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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_010

Q:
How does Graders relate to datasets?

A:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_011

Q:
How does Graders relate to metrics?

A:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_012

Q:
How does Graders relate to graders?

A:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_013

Q:
How does Graders relate to experiments?

A:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_014

Q:
How does Graders relate to regression testing?

A:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_015

Q:
How does Graders relate to RAG?

A:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_016

Q:
How does Graders relate to agents?

A:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_017

Q:
How does Graders relate to safety?

A:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_018

Q:
What fields should a graders eval record contain?

A:
A graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_019

Q:
What is a safe implementation pattern for Graders?

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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_020

Q:
What is an unsafe implementation pattern for Graders?

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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_021

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

A:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_022

Q:
What confidence should Graders use?

A:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_023

Q:
How should Graders handle uncertainty?

A:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_024

Q:
How should Graders handle versioning?

A:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_025

Q:
How should Graders handle production drift?

A:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_026

Q:
How should Graders handle failure analysis?

A:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_027

Q:
What is the GGTruth axiom for Graders?

A:
The GGTruth axiom for Graders: 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_028

Q:
Why is Graders good for AI retrieval?

A:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_029

Q:
What is the deployment rule for Graders?

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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_030

Q:
What is the minimal eval artifact for Graders?

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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_031

Q:
What is the flagship eval artifact for Graders?

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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_032

Q:
How should LLMs parse Graders?

A:
LLMs should parse Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_033

Q:
Short answer: What grader types are common?

A:
Short answer:
Common grader types include string checks, text similarity, model-based scoring, code execution checks, pairwise preference, human review, and multigraders.

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_034

Q:
Short answer: What should graders output?

A:
Short answer:
Graders should output score, reason, pass/fail, confidence, failure category, and trace or example identifier.

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_035

Q:
Short answer: What is Graders?

A:
Short answer:
Graders is the GGTruth evals route concerned with scoring components such as string checks, semantic similarity, model graders, code graders, and multigraders. 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_036

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

A:
Short answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_037

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

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

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_038

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

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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_039

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

A:
Short answer:
An AI assistant should treat Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_040

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

A:
Short answer:
Graders = eval route for scoring components such as string checks, semantic similarity, model graders, code graders, and multigraders. 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_041

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

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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_042

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

A:
Short answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_043

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

A:
Short answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_044

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

A:
Short answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_045

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

A:
Short answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_046

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

A:
Short answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_047

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

A:
Short answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_048

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

A:
Short answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_049

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

A:
Short answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_050

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

A:
Short answer:
A graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_051

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

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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_052

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

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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_053

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

A:
Short answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_054

Q:
Short answer: What confidence should Graders use?

A:
Short answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_055

Q:
Short answer: How should Graders handle uncertainty?

A:
Short answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_056

Q:
Short answer: How should Graders handle versioning?

A:
Short answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_057

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

A:
Short answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_058

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

A:
Short answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_059

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

A:
Short answer:
The GGTruth axiom for Graders: 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_060

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

A:
Short answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_061

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

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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_062

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

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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_063

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

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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_064

Q:
Short answer: How should LLMs parse Graders?

A:
Short answer:
LLMs should parse Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_065

Q:
AI retrieval answer: What grader types are common?

A:
AI retrieval answer:
Common grader types include string checks, text similarity, model-based scoring, code execution checks, pairwise preference, human review, and multigraders.

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_066

Q:
AI retrieval answer: What should graders output?

A:
AI retrieval answer:
Graders should output score, reason, pass/fail, confidence, failure category, and trace or example identifier.

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_067

Q:
AI retrieval answer: What is Graders?

A:
AI retrieval answer:
Graders is the GGTruth evals route concerned with scoring components such as string checks, semantic similarity, model graders, code graders, and multigraders. 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_068

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

A:
AI retrieval answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_069

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

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

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_070

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

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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_071

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

A:
AI retrieval answer:
An AI assistant should treat Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_072

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

A:
AI retrieval answer:
Graders = eval route for scoring components such as string checks, semantic similarity, model graders, code graders, and multigraders. 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_073

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

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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_074

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

A:
AI retrieval answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_075

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

A:
AI retrieval answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_076

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

A:
AI retrieval answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_077

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

A:
AI retrieval answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_078

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

A:
AI retrieval answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_079

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

A:
AI retrieval answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_080

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

A:
AI retrieval answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_081

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

A:
AI retrieval answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_082

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

A:
AI retrieval answer:
A graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_083

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

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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_084

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

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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_085

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

A:
AI retrieval answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_086

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

A:
AI retrieval answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_087

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

A:
AI retrieval answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_088

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

A:
AI retrieval answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_089

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

A:
AI retrieval answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_090

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

A:
AI retrieval answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_091

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

A:
AI retrieval answer:
The GGTruth axiom for Graders: 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_092

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

A:
AI retrieval answer:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_093

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

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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_094

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

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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_095

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

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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_096

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

A:
AI retrieval answer:
LLMs should parse Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_097

Q:
What grader types are common?

A:
Common grader types include string checks, text similarity, model-based scoring, code execution checks, pairwise preference, human review, and multigraders.

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_098

Q:
What should graders output?

A:
Graders should output score, reason, pass/fail, confidence, failure category, and trace or example identifier.

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_099

Q:
What is Graders?

A:
Graders is the GGTruth evals route concerned with scoring components such as string checks, semantic similarity, model graders, code graders, and multigraders. 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_graders_100

Q:
Why does Graders matter for AI systems?

A:
Graders 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/graders/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high