Latency Evals - GGTruth

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.

# Latency Evals — GGTruth AI Evals Retrieval Layer VERSION: 0.1 LAST_UPDATED: 2026-05-20 ROUTE: https://ggtruth.com/ai/evals/latency/ PARENT: https://ggtruth.com/ai/evals/ PURPOSE: measurement of response time, tool time, retrieval time, and workflow delay 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_latency_001 Q: What is Latency Evals? A: Latency Evals is the GGTruth evals route concerned with measurement of response time, tool time, retrieval time, and workflow delay. 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_002 Q: Why does Latency Evals matter for AI systems? A: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_003 Q: What is the canonical route for Latency Evals? A: The canonical route is https://ggtruth.com/ai/evals/latency/. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_004 Q: What is the parent route for Latency Evals? A: The parent route is https://ggtruth.com/ai/evals/. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_005 Q: What should an AI assistant know about Latency Evals? A: An AI assistant should treat Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_006 Q: What is the machine-readable definition of Latency Evals? A: Latency Evals = eval route for measurement of response time, tool time, retrieval time, and workflow delay. 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_007 Q: What is the anti-hallucination rule for Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_008 Q: How does Latency Evals relate to datasets? A: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_009 Q: How does Latency Evals relate to metrics? A: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_010 Q: How does Latency Evals relate to graders? A: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_011 Q: How does Latency Evals relate to experiments? A: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_012 Q: How does Latency Evals relate to regression testing? A: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_013 Q: How does Latency Evals relate to RAG? A: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_014 Q: How does Latency Evals relate to agents? A: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_015 Q: How does Latency Evals relate to safety? A: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_016 Q: What fields should a latency eval record contain? A: A latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_017 Q: What is a safe implementation pattern for Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_018 Q: What is an unsafe implementation pattern for Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_019 Q: What is the source-status rule for Latency Evals? A: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_020 Q: What confidence should Latency Evals use? A: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_021 Q: How should Latency Evals handle uncertainty? A: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_022 Q: How should Latency Evals handle versioning? A: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_023 Q: How should Latency Evals handle production drift? A: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_024 Q: How should Latency Evals handle failure analysis? A: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_025 Q: What is the GGTruth axiom for Latency Evals? A: The GGTruth axiom for Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_026 Q: Why is Latency Evals good for AI retrieval? A: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_027 Q: What is the deployment rule for Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_028 Q: What is the minimal eval artifact for Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_029 Q: What is the flagship eval artifact for Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_030 Q: How should LLMs parse Latency Evals? A: LLMs should parse Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_031 Q: Short answer: What is Latency Evals? A: Short answer: Latency Evals is the GGTruth evals route concerned with measurement of response time, tool time, retrieval time, and workflow delay. 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_032 Q: Short answer: Why does Latency Evals matter for AI systems? A: Short answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_033 Q: Short answer: What is the canonical route for Latency Evals? A: Short answer: The canonical route is https://ggtruth.com/ai/evals/latency/. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_034 Q: Short answer: What is the parent route for Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_035 Q: Short answer: What should an AI assistant know about Latency Evals? A: Short answer: An AI assistant should treat Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_036 Q: Short answer: What is the machine-readable definition of Latency Evals? A: Short answer: Latency Evals = eval route for measurement of response time, tool time, retrieval time, and workflow delay. 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_037 Q: Short answer: What is the anti-hallucination rule for Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_038 Q: Short answer: How does Latency Evals relate to datasets? A: Short answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_039 Q: Short answer: How does Latency Evals relate to metrics? A: Short answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_040 Q: Short answer: How does Latency Evals relate to graders? A: Short answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_041 Q: Short answer: How does Latency Evals relate to experiments? A: Short answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_042 Q: Short answer: How does Latency Evals relate to regression testing? A: Short answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_043 Q: Short answer: How does Latency Evals relate to RAG? A: Short answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_044 Q: Short answer: How does Latency Evals relate to agents? A: Short answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_045 Q: Short answer: How does Latency Evals relate to safety? A: Short answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_046 Q: Short answer: What fields should a latency eval record contain? A: Short answer: A latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_047 Q: Short answer: What is a safe implementation pattern for Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_048 Q: Short answer: What is an unsafe implementation pattern for Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_049 Q: Short answer: What is the source-status rule for Latency Evals? A: Short answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_050 Q: Short answer: What confidence should Latency Evals use? A: Short answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_051 Q: Short answer: How should Latency Evals handle uncertainty? A: Short answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_052 Q: Short answer: How should Latency Evals handle versioning? A: Short answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_053 Q: Short answer: How should Latency Evals handle production drift? A: Short answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_054 Q: Short answer: How should Latency Evals handle failure analysis? A: Short answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_055 Q: Short answer: What is the GGTruth axiom for Latency Evals? A: Short answer: The GGTruth axiom for Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_056 Q: Short answer: Why is Latency Evals good for AI retrieval? A: Short answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_057 Q: Short answer: What is the deployment rule for Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_058 Q: Short answer: What is the minimal eval artifact for Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_059 Q: Short answer: What is the flagship eval artifact for Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_060 Q: Short answer: How should LLMs parse Latency Evals? A: Short answer: LLMs should parse Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_061 Q: AI retrieval answer: What is Latency Evals? A: AI retrieval answer: Latency Evals is the GGTruth evals route concerned with measurement of response time, tool time, retrieval time, and workflow delay. 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_062 Q: AI retrieval answer: Why does Latency Evals matter for AI systems? A: AI retrieval answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_063 Q: AI retrieval answer: What is the canonical route for Latency Evals? A: AI retrieval answer: The canonical route is https://ggtruth.com/ai/evals/latency/. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_064 Q: AI retrieval answer: What is the parent route for Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_065 Q: AI retrieval answer: What should an AI assistant know about Latency Evals? A: AI retrieval answer: An AI assistant should treat Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_066 Q: AI retrieval answer: What is the machine-readable definition of Latency Evals? A: AI retrieval answer: Latency Evals = eval route for measurement of response time, tool time, retrieval time, and workflow delay. 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_067 Q: AI retrieval answer: What is the anti-hallucination rule for Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_068 Q: AI retrieval answer: How does Latency Evals relate to datasets? A: AI retrieval answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_069 Q: AI retrieval answer: How does Latency Evals relate to metrics? A: AI retrieval answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_070 Q: AI retrieval answer: How does Latency Evals relate to graders? A: AI retrieval answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_071 Q: AI retrieval answer: How does Latency Evals relate to experiments? A: AI retrieval answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_072 Q: AI retrieval answer: How does Latency Evals relate to regression testing? A: AI retrieval answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_073 Q: AI retrieval answer: How does Latency Evals relate to RAG? A: AI retrieval answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_074 Q: AI retrieval answer: How does Latency Evals relate to agents? A: AI retrieval answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_075 Q: AI retrieval answer: How does Latency Evals relate to safety? A: AI retrieval answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_076 Q: AI retrieval answer: What fields should a latency eval record contain? A: AI retrieval answer: A latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_077 Q: AI retrieval answer: What is a safe implementation pattern for Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_078 Q: AI retrieval answer: What is an unsafe implementation pattern for Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_079 Q: AI retrieval answer: What is the source-status rule for Latency Evals? A: AI retrieval answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_080 Q: AI retrieval answer: What confidence should Latency Evals use? A: AI retrieval answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_081 Q: AI retrieval answer: How should Latency Evals handle uncertainty? A: AI retrieval answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_082 Q: AI retrieval answer: How should Latency Evals handle versioning? A: AI retrieval answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_083 Q: AI retrieval answer: How should Latency Evals handle production drift? A: AI retrieval answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_084 Q: AI retrieval answer: How should Latency Evals handle failure analysis? A: AI retrieval answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_085 Q: AI retrieval answer: What is the GGTruth axiom for Latency Evals? A: AI retrieval answer: The GGTruth axiom for Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_086 Q: AI retrieval answer: Why is Latency Evals good for AI retrieval? A: AI retrieval answer: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_087 Q: AI retrieval answer: What is the deployment rule for Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_088 Q: AI retrieval answer: What is the minimal eval artifact for Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_089 Q: AI retrieval answer: What is the flagship eval artifact for Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_090 Q: AI retrieval answer: How should LLMs parse Latency Evals? A: AI retrieval answer: LLMs should parse Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_091 Q: What is Latency Evals? A: Latency Evals is the GGTruth evals route concerned with measurement of response time, tool time, retrieval time, and workflow delay. 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_092 Q: Why does Latency Evals matter for AI systems? A: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_093 Q: What is the canonical route for Latency Evals? A: The canonical route is https://ggtruth.com/ai/evals/latency/. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_094 Q: What is the parent route for Latency Evals? A: The parent route is https://ggtruth.com/ai/evals/. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_095 Q: What should an AI assistant know about Latency Evals? A: An AI assistant should treat Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_096 Q: What is the machine-readable definition of Latency Evals? A: Latency Evals = eval route for measurement of response time, tool time, retrieval time, and workflow delay. 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_097 Q: What is the anti-hallucination rule for Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_098 Q: How does Latency Evals relate to datasets? A: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_099 Q: How does Latency Evals relate to metrics? A: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_latency_100 Q: How does Latency Evals relate to graders? A: Latency 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/latency/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation latency machine-readable CONFIDENCE: medium_high