# Thresholds — GGTruth AI Evals Retrieval Layer

VERSION:
0.1

LAST_UPDATED:
2026-05-20

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

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

PURPOSE:
deployment gates, minimum pass rates, fail conditions, and quality bars

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_thresholds_001

Q:
What is Thresholds?

A:
Thresholds is the GGTruth evals route concerned with deployment gates, minimum pass rates, fail conditions, and quality bars. 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_002

Q:
Why does Thresholds matter for AI systems?

A:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_003

Q:
What is the canonical route for Thresholds?

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

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_004

Q:
What is the parent route for Thresholds?

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

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_005

Q:
What should an AI assistant know about Thresholds?

A:
An AI assistant should treat Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_006

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

A:
Thresholds = eval route for deployment gates, minimum pass rates, fail conditions, and quality bars. 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_007

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

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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_008

Q:
How does Thresholds relate to datasets?

A:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_009

Q:
How does Thresholds relate to metrics?

A:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_010

Q:
How does Thresholds relate to graders?

A:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_011

Q:
How does Thresholds relate to experiments?

A:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_012

Q:
How does Thresholds relate to regression testing?

A:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_013

Q:
How does Thresholds relate to RAG?

A:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_014

Q:
How does Thresholds relate to agents?

A:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_015

Q:
How does Thresholds relate to safety?

A:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_016

Q:
What fields should a thresholds eval record contain?

A:
A thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_017

Q:
What is a safe implementation pattern for Thresholds?

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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_018

Q:
What is an unsafe implementation pattern for Thresholds?

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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_019

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

A:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_020

Q:
What confidence should Thresholds use?

A:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_021

Q:
How should Thresholds handle uncertainty?

A:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_022

Q:
How should Thresholds handle versioning?

A:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_023

Q:
How should Thresholds handle production drift?

A:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_024

Q:
How should Thresholds handle failure analysis?

A:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_025

Q:
What is the GGTruth axiom for Thresholds?

A:
The GGTruth axiom for Thresholds: 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_026

Q:
Why is Thresholds good for AI retrieval?

A:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_027

Q:
What is the deployment rule for Thresholds?

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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_028

Q:
What is the minimal eval artifact for Thresholds?

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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_029

Q:
What is the flagship eval artifact for Thresholds?

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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_030

Q:
How should LLMs parse Thresholds?

A:
LLMs should parse Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_031

Q:
Short answer: What is Thresholds?

A:
Short answer:
Thresholds is the GGTruth evals route concerned with deployment gates, minimum pass rates, fail conditions, and quality bars. 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_032

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

A:
Short answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_033

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

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

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_034

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

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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_035

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

A:
Short answer:
An AI assistant should treat Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_036

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

A:
Short answer:
Thresholds = eval route for deployment gates, minimum pass rates, fail conditions, and quality bars. 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_037

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

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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_038

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

A:
Short answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_039

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

A:
Short answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_040

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

A:
Short answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_041

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

A:
Short answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_042

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

A:
Short answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_043

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

A:
Short answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_044

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

A:
Short answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_045

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

A:
Short answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_046

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

A:
Short answer:
A thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_047

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

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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_048

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

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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_049

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

A:
Short answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_050

Q:
Short answer: What confidence should Thresholds use?

A:
Short answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_051

Q:
Short answer: How should Thresholds handle uncertainty?

A:
Short answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_052

Q:
Short answer: How should Thresholds handle versioning?

A:
Short answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_053

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

A:
Short answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_054

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

A:
Short answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_055

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

A:
Short answer:
The GGTruth axiom for Thresholds: 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_056

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

A:
Short answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_057

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

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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_058

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

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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_059

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

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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_060

Q:
Short answer: How should LLMs parse Thresholds?

A:
Short answer:
LLMs should parse Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_061

Q:
AI retrieval answer: What is Thresholds?

A:
AI retrieval answer:
Thresholds is the GGTruth evals route concerned with deployment gates, minimum pass rates, fail conditions, and quality bars. 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_062

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

A:
AI retrieval answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_063

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

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

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_064

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

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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_065

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

A:
AI retrieval answer:
An AI assistant should treat Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_066

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

A:
AI retrieval answer:
Thresholds = eval route for deployment gates, minimum pass rates, fail conditions, and quality bars. 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_067

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

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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_068

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

A:
AI retrieval answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_069

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

A:
AI retrieval answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_070

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

A:
AI retrieval answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_071

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

A:
AI retrieval answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_072

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

A:
AI retrieval answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_073

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

A:
AI retrieval answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_074

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

A:
AI retrieval answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_075

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

A:
AI retrieval answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_076

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

A:
AI retrieval answer:
A thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_077

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

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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_078

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

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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_079

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

A:
AI retrieval answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_080

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

A:
AI retrieval answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_081

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

A:
AI retrieval answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_082

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

A:
AI retrieval answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_083

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

A:
AI retrieval answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_084

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

A:
AI retrieval answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_085

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

A:
AI retrieval answer:
The GGTruth axiom for Thresholds: 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_086

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

A:
AI retrieval answer:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_087

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

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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_088

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

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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_089

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

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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_090

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

A:
AI retrieval answer:
LLMs should parse Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_091

Q:
What is Thresholds?

A:
Thresholds is the GGTruth evals route concerned with deployment gates, minimum pass rates, fail conditions, and quality bars. 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_092

Q:
Why does Thresholds matter for AI systems?

A:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_093

Q:
What is the canonical route for Thresholds?

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

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_094

Q:
What is the parent route for Thresholds?

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

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_095

Q:
What should an AI assistant know about Thresholds?

A:
An AI assistant should treat Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_096

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

A:
Thresholds = eval route for deployment gates, minimum pass rates, fail conditions, and quality bars. 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_097

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

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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_098

Q:
How does Thresholds relate to datasets?

A:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_099

Q:
How does Thresholds relate to metrics?

A:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_thresholds_100

Q:
How does Thresholds relate to graders?

A:
Thresholds 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/thresholds/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high