# Scorecards — GGTruth AI Evals Retrieval Layer

VERSION:
0.1

LAST_UPDATED:
2026-05-20

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

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

PURPOSE:
summary reports that combine metrics, thresholds, regressions, and deployment decisions

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_scorecards_001

Q:
What is Scorecards?

A:
Scorecards is the GGTruth evals route concerned with summary reports that combine metrics, thresholds, regressions, and deployment decisions. 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_002

Q:
Why does Scorecards matter for AI systems?

A:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_003

Q:
What is the canonical route for Scorecards?

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

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_004

Q:
What is the parent route for Scorecards?

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

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_005

Q:
What should an AI assistant know about Scorecards?

A:
An AI assistant should treat Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_006

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

A:
Scorecards = eval route for summary reports that combine metrics, thresholds, regressions, and deployment decisions. 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_007

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

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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_008

Q:
How does Scorecards relate to datasets?

A:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_009

Q:
How does Scorecards relate to metrics?

A:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_010

Q:
How does Scorecards relate to graders?

A:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_011

Q:
How does Scorecards relate to experiments?

A:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_012

Q:
How does Scorecards relate to regression testing?

A:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_013

Q:
How does Scorecards relate to RAG?

A:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_014

Q:
How does Scorecards relate to agents?

A:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_015

Q:
How does Scorecards relate to safety?

A:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_016

Q:
What fields should a scorecards eval record contain?

A:
A scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_017

Q:
What is a safe implementation pattern for Scorecards?

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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_018

Q:
What is an unsafe implementation pattern for Scorecards?

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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_019

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

A:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_020

Q:
What confidence should Scorecards use?

A:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_021

Q:
How should Scorecards handle uncertainty?

A:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_022

Q:
How should Scorecards handle versioning?

A:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_023

Q:
How should Scorecards handle production drift?

A:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_024

Q:
How should Scorecards handle failure analysis?

A:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_025

Q:
What is the GGTruth axiom for Scorecards?

A:
The GGTruth axiom for Scorecards: 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_026

Q:
Why is Scorecards good for AI retrieval?

A:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_027

Q:
What is the deployment rule for Scorecards?

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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_028

Q:
What is the minimal eval artifact for Scorecards?

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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_029

Q:
What is the flagship eval artifact for Scorecards?

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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_030

Q:
How should LLMs parse Scorecards?

A:
LLMs should parse Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_031

Q:
Short answer: What is Scorecards?

A:
Short answer:
Scorecards is the GGTruth evals route concerned with summary reports that combine metrics, thresholds, regressions, and deployment decisions. 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_032

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

A:
Short answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_033

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

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

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_034

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

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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_035

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

A:
Short answer:
An AI assistant should treat Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_036

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

A:
Short answer:
Scorecards = eval route for summary reports that combine metrics, thresholds, regressions, and deployment decisions. 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_037

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

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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_038

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

A:
Short answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_039

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

A:
Short answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_040

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

A:
Short answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_041

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

A:
Short answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_042

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

A:
Short answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_043

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

A:
Short answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_044

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

A:
Short answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_045

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

A:
Short answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_046

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

A:
Short answer:
A scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_047

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

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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_048

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

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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_049

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

A:
Short answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_050

Q:
Short answer: What confidence should Scorecards use?

A:
Short answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_051

Q:
Short answer: How should Scorecards handle uncertainty?

A:
Short answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_052

Q:
Short answer: How should Scorecards handle versioning?

A:
Short answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_053

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

A:
Short answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_054

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

A:
Short answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_055

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

A:
Short answer:
The GGTruth axiom for Scorecards: 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_056

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

A:
Short answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_057

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

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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_058

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

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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_059

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

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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_060

Q:
Short answer: How should LLMs parse Scorecards?

A:
Short answer:
LLMs should parse Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_061

Q:
AI retrieval answer: What is Scorecards?

A:
AI retrieval answer:
Scorecards is the GGTruth evals route concerned with summary reports that combine metrics, thresholds, regressions, and deployment decisions. 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_062

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

A:
AI retrieval answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_063

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

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

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_064

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

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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_065

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

A:
AI retrieval answer:
An AI assistant should treat Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_066

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

A:
AI retrieval answer:
Scorecards = eval route for summary reports that combine metrics, thresholds, regressions, and deployment decisions. 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_067

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

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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_068

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

A:
AI retrieval answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_069

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

A:
AI retrieval answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_070

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

A:
AI retrieval answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_071

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

A:
AI retrieval answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_072

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

A:
AI retrieval answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_073

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

A:
AI retrieval answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_074

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

A:
AI retrieval answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_075

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

A:
AI retrieval answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_076

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

A:
AI retrieval answer:
A scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_077

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

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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_078

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

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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_079

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

A:
AI retrieval answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_080

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

A:
AI retrieval answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_081

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

A:
AI retrieval answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_082

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

A:
AI retrieval answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_083

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

A:
AI retrieval answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_084

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

A:
AI retrieval answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_085

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

A:
AI retrieval answer:
The GGTruth axiom for Scorecards: 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_086

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

A:
AI retrieval answer:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_087

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

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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_088

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

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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_089

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

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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_090

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

A:
AI retrieval answer:
LLMs should parse Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_091

Q:
What is Scorecards?

A:
Scorecards is the GGTruth evals route concerned with summary reports that combine metrics, thresholds, regressions, and deployment decisions. 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_092

Q:
Why does Scorecards matter for AI systems?

A:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_093

Q:
What is the canonical route for Scorecards?

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

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_094

Q:
What is the parent route for Scorecards?

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

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_095

Q:
What should an AI assistant know about Scorecards?

A:
An AI assistant should treat Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_096

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

A:
Scorecards = eval route for summary reports that combine metrics, thresholds, regressions, and deployment decisions. 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_097

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

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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_098

Q:
How does Scorecards relate to datasets?

A:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_099

Q:
How does Scorecards relate to metrics?

A:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_scorecards_100

Q:
How does Scorecards relate to graders?

A:
Scorecards 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/scorecards/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high