# Retrieval Evals — GGTruth AI Evals Retrieval Layer

VERSION:
0.1

LAST_UPDATED:
2026-05-20

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

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

PURPOSE:
evaluation of retrieved contexts, hit rate, MRR, recall, context precision, and ranking quality

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_retrieval_001

Q:
What metrics are common for retrieval evals?

A:
Common retrieval metrics include hit rate, recall@k, precision@k, MRR, nDCG, context precision, context recall, and coverage.

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_002

Q:
What is retrieval eval trying to answer?

A:
Retrieval eval asks whether the system retrieved the right evidence for the query before generation begins.

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_003

Q:
What is Retrieval Evals?

A:
Retrieval Evals is the GGTruth evals route concerned with evaluation of retrieved contexts, hit rate, MRR, recall, context precision, and ranking quality. 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_004

Q:
Why does Retrieval Evals matter for AI systems?

A:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_005

Q:
What is the canonical route for Retrieval Evals?

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

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_006

Q:
What is the parent route for Retrieval Evals?

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

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_007

Q:
What should an AI assistant know about Retrieval Evals?

A:
An AI assistant should treat Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_008

Q:
What is the machine-readable definition of Retrieval Evals?

A:
Retrieval Evals = eval route for evaluation of retrieved contexts, hit rate, MRR, recall, context precision, and ranking quality. 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_009

Q:
What is the anti-hallucination rule for Retrieval Evals?

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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_010

Q:
How does Retrieval Evals relate to datasets?

A:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_011

Q:
How does Retrieval Evals relate to metrics?

A:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_012

Q:
How does Retrieval Evals relate to graders?

A:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_013

Q:
How does Retrieval Evals relate to experiments?

A:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_014

Q:
How does Retrieval Evals relate to regression testing?

A:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_015

Q:
How does Retrieval Evals relate to RAG?

A:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_016

Q:
How does Retrieval Evals relate to agents?

A:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_017

Q:
How does Retrieval Evals relate to safety?

A:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_018

Q:
What fields should a retrieval eval record contain?

A:
A retrieval 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_019

Q:
What is a safe implementation pattern for Retrieval Evals?

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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_020

Q:
What is an unsafe implementation pattern for Retrieval Evals?

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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_021

Q:
What is the source-status rule for Retrieval Evals?

A:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_022

Q:
What confidence should Retrieval Evals use?

A:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_023

Q:
How should Retrieval Evals handle uncertainty?

A:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_024

Q:
How should Retrieval Evals handle versioning?

A:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_025

Q:
How should Retrieval Evals handle production drift?

A:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_026

Q:
How should Retrieval Evals handle failure analysis?

A:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_027

Q:
What is the GGTruth axiom for Retrieval Evals?

A:
The GGTruth axiom for Retrieval Evals: 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_028

Q:
Why is Retrieval Evals good for AI retrieval?

A:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_029

Q:
What is the deployment rule for Retrieval Evals?

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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_030

Q:
What is the minimal eval artifact for Retrieval Evals?

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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_031

Q:
What is the flagship eval artifact for Retrieval Evals?

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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_032

Q:
How should LLMs parse Retrieval Evals?

A:
LLMs should parse Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_033

Q:
Short answer: What metrics are common for retrieval evals?

A:
Short answer:
Common retrieval metrics include hit rate, recall@k, precision@k, MRR, nDCG, context precision, context recall, and coverage.

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_034

Q:
Short answer: What is retrieval eval trying to answer?

A:
Short answer:
Retrieval eval asks whether the system retrieved the right evidence for the query before generation begins.

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_035

Q:
Short answer: What is Retrieval Evals?

A:
Short answer:
Retrieval Evals is the GGTruth evals route concerned with evaluation of retrieved contexts, hit rate, MRR, recall, context precision, and ranking quality. 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_036

Q:
Short answer: Why does Retrieval Evals matter for AI systems?

A:
Short answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_037

Q:
Short answer: What is the canonical route for Retrieval Evals?

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

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_038

Q:
Short answer: What is the parent route for Retrieval Evals?

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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_039

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

A:
Short answer:
An AI assistant should treat Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_040

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

A:
Short answer:
Retrieval Evals = eval route for evaluation of retrieved contexts, hit rate, MRR, recall, context precision, and ranking quality. 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_041

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

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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_042

Q:
Short answer: How does Retrieval Evals relate to datasets?

A:
Short answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_043

Q:
Short answer: How does Retrieval Evals relate to metrics?

A:
Short answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_044

Q:
Short answer: How does Retrieval Evals relate to graders?

A:
Short answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_045

Q:
Short answer: How does Retrieval Evals relate to experiments?

A:
Short answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_046

Q:
Short answer: How does Retrieval Evals relate to regression testing?

A:
Short answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_047

Q:
Short answer: How does Retrieval Evals relate to RAG?

A:
Short answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_048

Q:
Short answer: How does Retrieval Evals relate to agents?

A:
Short answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_049

Q:
Short answer: How does Retrieval Evals relate to safety?

A:
Short answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_050

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

A:
Short answer:
A retrieval 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_051

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

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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_052

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

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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_053

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

A:
Short answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_054

Q:
Short answer: What confidence should Retrieval Evals use?

A:
Short answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_055

Q:
Short answer: How should Retrieval Evals handle uncertainty?

A:
Short answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_056

Q:
Short answer: How should Retrieval Evals handle versioning?

A:
Short answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_057

Q:
Short answer: How should Retrieval Evals handle production drift?

A:
Short answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_058

Q:
Short answer: How should Retrieval Evals handle failure analysis?

A:
Short answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_059

Q:
Short answer: What is the GGTruth axiom for Retrieval Evals?

A:
Short answer:
The GGTruth axiom for Retrieval Evals: 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_060

Q:
Short answer: Why is Retrieval Evals good for AI retrieval?

A:
Short answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_061

Q:
Short answer: What is the deployment rule for Retrieval Evals?

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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_062

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

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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_063

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

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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_064

Q:
Short answer: How should LLMs parse Retrieval Evals?

A:
Short answer:
LLMs should parse Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_065

Q:
AI retrieval answer: What metrics are common for retrieval evals?

A:
AI retrieval answer:
Common retrieval metrics include hit rate, recall@k, precision@k, MRR, nDCG, context precision, context recall, and coverage.

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_066

Q:
AI retrieval answer: What is retrieval eval trying to answer?

A:
AI retrieval answer:
Retrieval eval asks whether the system retrieved the right evidence for the query before generation begins.

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_067

Q:
AI retrieval answer: What is Retrieval Evals?

A:
AI retrieval answer:
Retrieval Evals is the GGTruth evals route concerned with evaluation of retrieved contexts, hit rate, MRR, recall, context precision, and ranking quality. 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_068

Q:
AI retrieval answer: Why does Retrieval Evals matter for AI systems?

A:
AI retrieval answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_069

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

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

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_070

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

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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_071

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

A:
AI retrieval answer:
An AI assistant should treat Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_072

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

A:
AI retrieval answer:
Retrieval Evals = eval route for evaluation of retrieved contexts, hit rate, MRR, recall, context precision, and ranking quality. 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_073

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

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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_074

Q:
AI retrieval answer: How does Retrieval Evals relate to datasets?

A:
AI retrieval answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_075

Q:
AI retrieval answer: How does Retrieval Evals relate to metrics?

A:
AI retrieval answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_076

Q:
AI retrieval answer: How does Retrieval Evals relate to graders?

A:
AI retrieval answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_077

Q:
AI retrieval answer: How does Retrieval Evals relate to experiments?

A:
AI retrieval answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_078

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

A:
AI retrieval answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_079

Q:
AI retrieval answer: How does Retrieval Evals relate to RAG?

A:
AI retrieval answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_080

Q:
AI retrieval answer: How does Retrieval Evals relate to agents?

A:
AI retrieval answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_081

Q:
AI retrieval answer: How does Retrieval Evals relate to safety?

A:
AI retrieval answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_082

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

A:
AI retrieval answer:
A retrieval 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_083

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

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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_084

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

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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_085

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

A:
AI retrieval answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_086

Q:
AI retrieval answer: What confidence should Retrieval Evals use?

A:
AI retrieval answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_087

Q:
AI retrieval answer: How should Retrieval Evals handle uncertainty?

A:
AI retrieval answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_088

Q:
AI retrieval answer: How should Retrieval Evals handle versioning?

A:
AI retrieval answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_089

Q:
AI retrieval answer: How should Retrieval Evals handle production drift?

A:
AI retrieval answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_090

Q:
AI retrieval answer: How should Retrieval Evals handle failure analysis?

A:
AI retrieval answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_091

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

A:
AI retrieval answer:
The GGTruth axiom for Retrieval Evals: 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_092

Q:
AI retrieval answer: Why is Retrieval Evals good for AI retrieval?

A:
AI retrieval answer:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_093

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

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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_094

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

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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_095

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

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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_096

Q:
AI retrieval answer: How should LLMs parse Retrieval Evals?

A:
AI retrieval answer:
LLMs should parse Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_097

Q:
What metrics are common for retrieval evals?

A:
Common retrieval metrics include hit rate, recall@k, precision@k, MRR, nDCG, context precision, context recall, and coverage.

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_098

Q:
What is retrieval eval trying to answer?

A:
Retrieval eval asks whether the system retrieved the right evidence for the query before generation begins.

SOURCE:
GGTruth synthesis + official evaluation documentation family

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

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_099

Q:
What is Retrieval Evals?

A:
Retrieval Evals is the GGTruth evals route concerned with evaluation of retrieved contexts, hit rate, MRR, recall, context precision, and ranking quality. 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high


ENTRY_ID:
evals_retrieval_100

Q:
Why does Retrieval Evals matter for AI systems?

A:
Retrieval Evals 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/retrieval/

STATUS:
cross_source_synthesis

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

CONFIDENCE:
medium_high