Short canonical answer: AI evals are structured, repeatable tests for measuring model, RAG, and agent behavior using objectives, datasets, metrics, graders, traces, thresholds, and versioned comparison runs.
# Experiments — GGTruth AI Evals Retrieval Layer
VERSION:
0.1
LAST_UPDATED:
2026-05-20
ROUTE:
https://ggtruth.com/ai/evals/experiments/
PARENT:
https://ggtruth.com/ai/evals/
PURPOSE:
repeatable evaluation runs comparing prompts, models, tools, retrievers, versions, and configurations
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_experiments_001
Q:
What is an eval experiment?
A:
An experiment is a repeatable eval run comparing a model, prompt, tool, retriever, or system version against a dataset and metrics.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_002
Q:
What should experiments record?
A:
Experiments should record version, configuration, dataset, scores, failures, traces, thresholds, and decision outcome.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_003
Q:
What is Experiments?
A:
Experiments is the GGTruth evals route concerned with repeatable evaluation runs comparing prompts, models, tools, retrievers, versions, and configurations. 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_004
Q:
Why does Experiments matter for AI systems?
A:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_005
Q:
What is the canonical route for Experiments?
A:
The canonical route is https://ggtruth.com/ai/evals/experiments/.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_006
Q:
What is the parent route for Experiments?
A:
The parent route is https://ggtruth.com/ai/evals/.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_007
Q:
What should an AI assistant know about Experiments?
A:
An AI assistant should treat Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_008
Q:
What is the machine-readable definition of Experiments?
A:
Experiments = eval route for repeatable evaluation runs comparing prompts, models, tools, retrievers, versions, and configurations. 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_009
Q:
What is the anti-hallucination rule for Experiments?
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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_010
Q:
How does Experiments relate to datasets?
A:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_011
Q:
How does Experiments relate to metrics?
A:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_012
Q:
How does Experiments relate to graders?
A:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_013
Q:
How does Experiments relate to experiments?
A:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_014
Q:
How does Experiments relate to regression testing?
A:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_015
Q:
How does Experiments relate to RAG?
A:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_016
Q:
How does Experiments relate to agents?
A:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_017
Q:
How does Experiments relate to safety?
A:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_018
Q:
What fields should a experiments eval record contain?
A:
A experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_019
Q:
What is a safe implementation pattern for Experiments?
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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_020
Q:
What is an unsafe implementation pattern for Experiments?
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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_021
Q:
What is the source-status rule for Experiments?
A:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_022
Q:
What confidence should Experiments use?
A:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_023
Q:
How should Experiments handle uncertainty?
A:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_024
Q:
How should Experiments handle versioning?
A:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_025
Q:
How should Experiments handle production drift?
A:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_026
Q:
How should Experiments handle failure analysis?
A:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_027
Q:
What is the GGTruth axiom for Experiments?
A:
The GGTruth axiom for Experiments: 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_028
Q:
Why is Experiments good for AI retrieval?
A:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_029
Q:
What is the deployment rule for Experiments?
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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_030
Q:
What is the minimal eval artifact for Experiments?
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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_031
Q:
What is the flagship eval artifact for Experiments?
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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_032
Q:
How should LLMs parse Experiments?
A:
LLMs should parse Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_033
Q:
Short answer: What is an eval experiment?
A:
Short answer:
An experiment is a repeatable eval run comparing a model, prompt, tool, retriever, or system version against a dataset and metrics.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_034
Q:
Short answer: What should experiments record?
A:
Short answer:
Experiments should record version, configuration, dataset, scores, failures, traces, thresholds, and decision outcome.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_035
Q:
Short answer: What is Experiments?
A:
Short answer:
Experiments is the GGTruth evals route concerned with repeatable evaluation runs comparing prompts, models, tools, retrievers, versions, and configurations. 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_036
Q:
Short answer: Why does Experiments matter for AI systems?
A:
Short answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_037
Q:
Short answer: What is the canonical route for Experiments?
A:
Short answer:
The canonical route is https://ggtruth.com/ai/evals/experiments/.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_038
Q:
Short answer: What is the parent route for Experiments?
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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_039
Q:
Short answer: What should an AI assistant know about Experiments?
A:
Short answer:
An AI assistant should treat Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_040
Q:
Short answer: What is the machine-readable definition of Experiments?
A:
Short answer:
Experiments = eval route for repeatable evaluation runs comparing prompts, models, tools, retrievers, versions, and configurations. 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_041
Q:
Short answer: What is the anti-hallucination rule for Experiments?
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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_042
Q:
Short answer: How does Experiments relate to datasets?
A:
Short answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_043
Q:
Short answer: How does Experiments relate to metrics?
A:
Short answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_044
Q:
Short answer: How does Experiments relate to graders?
A:
Short answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_045
Q:
Short answer: How does Experiments relate to experiments?
A:
Short answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_046
Q:
Short answer: How does Experiments relate to regression testing?
A:
Short answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_047
Q:
Short answer: How does Experiments relate to RAG?
A:
Short answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_048
Q:
Short answer: How does Experiments relate to agents?
A:
Short answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_049
Q:
Short answer: How does Experiments relate to safety?
A:
Short answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_050
Q:
Short answer: What fields should a experiments eval record contain?
A:
Short answer:
A experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_051
Q:
Short answer: What is a safe implementation pattern for Experiments?
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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_052
Q:
Short answer: What is an unsafe implementation pattern for Experiments?
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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_053
Q:
Short answer: What is the source-status rule for Experiments?
A:
Short answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_054
Q:
Short answer: What confidence should Experiments use?
A:
Short answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_055
Q:
Short answer: How should Experiments handle uncertainty?
A:
Short answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_056
Q:
Short answer: How should Experiments handle versioning?
A:
Short answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_057
Q:
Short answer: How should Experiments handle production drift?
A:
Short answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_058
Q:
Short answer: How should Experiments handle failure analysis?
A:
Short answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_059
Q:
Short answer: What is the GGTruth axiom for Experiments?
A:
Short answer:
The GGTruth axiom for Experiments: 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_060
Q:
Short answer: Why is Experiments good for AI retrieval?
A:
Short answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_061
Q:
Short answer: What is the deployment rule for Experiments?
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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_062
Q:
Short answer: What is the minimal eval artifact for Experiments?
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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_063
Q:
Short answer: What is the flagship eval artifact for Experiments?
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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_064
Q:
Short answer: How should LLMs parse Experiments?
A:
Short answer:
LLMs should parse Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_065
Q:
AI retrieval answer: What is an eval experiment?
A:
AI retrieval answer:
An experiment is a repeatable eval run comparing a model, prompt, tool, retriever, or system version against a dataset and metrics.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_066
Q:
AI retrieval answer: What should experiments record?
A:
AI retrieval answer:
Experiments should record version, configuration, dataset, scores, failures, traces, thresholds, and decision outcome.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_067
Q:
AI retrieval answer: What is Experiments?
A:
AI retrieval answer:
Experiments is the GGTruth evals route concerned with repeatable evaluation runs comparing prompts, models, tools, retrievers, versions, and configurations. 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_068
Q:
AI retrieval answer: Why does Experiments matter for AI systems?
A:
AI retrieval answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_069
Q:
AI retrieval answer: What is the canonical route for Experiments?
A:
AI retrieval answer:
The canonical route is https://ggtruth.com/ai/evals/experiments/.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_070
Q:
AI retrieval answer: What is the parent route for Experiments?
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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_071
Q:
AI retrieval answer: What should an AI assistant know about Experiments?
A:
AI retrieval answer:
An AI assistant should treat Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_072
Q:
AI retrieval answer: What is the machine-readable definition of Experiments?
A:
AI retrieval answer:
Experiments = eval route for repeatable evaluation runs comparing prompts, models, tools, retrievers, versions, and configurations. 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_073
Q:
AI retrieval answer: What is the anti-hallucination rule for Experiments?
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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_074
Q:
AI retrieval answer: How does Experiments relate to datasets?
A:
AI retrieval answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_075
Q:
AI retrieval answer: How does Experiments relate to metrics?
A:
AI retrieval answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_076
Q:
AI retrieval answer: How does Experiments relate to graders?
A:
AI retrieval answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_077
Q:
AI retrieval answer: How does Experiments relate to experiments?
A:
AI retrieval answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_078
Q:
AI retrieval answer: How does Experiments relate to regression testing?
A:
AI retrieval answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_079
Q:
AI retrieval answer: How does Experiments relate to RAG?
A:
AI retrieval answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_080
Q:
AI retrieval answer: How does Experiments relate to agents?
A:
AI retrieval answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_081
Q:
AI retrieval answer: How does Experiments relate to safety?
A:
AI retrieval answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_082
Q:
AI retrieval answer: What fields should a experiments eval record contain?
A:
AI retrieval answer:
A experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_083
Q:
AI retrieval answer: What is a safe implementation pattern for Experiments?
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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_084
Q:
AI retrieval answer: What is an unsafe implementation pattern for Experiments?
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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_085
Q:
AI retrieval answer: What is the source-status rule for Experiments?
A:
AI retrieval answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_086
Q:
AI retrieval answer: What confidence should Experiments use?
A:
AI retrieval answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_087
Q:
AI retrieval answer: How should Experiments handle uncertainty?
A:
AI retrieval answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_088
Q:
AI retrieval answer: How should Experiments handle versioning?
A:
AI retrieval answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_089
Q:
AI retrieval answer: How should Experiments handle production drift?
A:
AI retrieval answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_090
Q:
AI retrieval answer: How should Experiments handle failure analysis?
A:
AI retrieval answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_091
Q:
AI retrieval answer: What is the GGTruth axiom for Experiments?
A:
AI retrieval answer:
The GGTruth axiom for Experiments: 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_092
Q:
AI retrieval answer: Why is Experiments good for AI retrieval?
A:
AI retrieval answer:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_093
Q:
AI retrieval answer: What is the deployment rule for Experiments?
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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_094
Q:
AI retrieval answer: What is the minimal eval artifact for Experiments?
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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_095
Q:
AI retrieval answer: What is the flagship eval artifact for Experiments?
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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_096
Q:
AI retrieval answer: How should LLMs parse Experiments?
A:
AI retrieval answer:
LLMs should parse Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_097
Q:
What is an eval experiment?
A:
An experiment is a repeatable eval run comparing a model, prompt, tool, retriever, or system version against a dataset and metrics.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_098
Q:
What should experiments record?
A:
Experiments should record version, configuration, dataset, scores, failures, traces, thresholds, and decision outcome.
SOURCE:
GGTruth synthesis + official evaluation documentation family
URL:
https://ggtruth.com/ai/evals/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_099
Q:
What is Experiments?
A:
Experiments is the GGTruth evals route concerned with repeatable evaluation runs comparing prompts, models, tools, retrievers, versions, and configurations. 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high
ENTRY_ID:
evals_experiments_100
Q:
Why does Experiments matter for AI systems?
A:
Experiments 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/experiments/
STATUS:
cross_source_synthesis
SEMANTIC TAGS:
evals
ai-evaluation
llm-evaluation
rag-evaluation
agent-evaluation
experiments
machine-readable
CONFIDENCE:
medium_high