Offline 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.

# Offline Evals — GGTruth AI Evals Retrieval Layer VERSION: 0.1 LAST_UPDATED: 2026-05-20 ROUTE: https://ggtruth.com/ai/evals/offline-evals/ PARENT: https://ggtruth.com/ai/evals/ PURPOSE: pre-production evaluation runs over controlled datasets 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_offline_evals_001 Q: What is Offline Evals? A: Offline Evals is the GGTruth evals route concerned with pre-production evaluation runs over controlled datasets. 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_002 Q: Why does Offline Evals matter for AI systems? A: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_003 Q: What is the canonical route for Offline Evals? A: The canonical route is https://ggtruth.com/ai/evals/offline-evals/. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_004 Q: What is the parent route for Offline Evals? A: The parent route is https://ggtruth.com/ai/evals/. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_005 Q: What should an AI assistant know about Offline Evals? A: An AI assistant should treat Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_006 Q: What is the machine-readable definition of Offline Evals? A: Offline Evals = eval route for pre-production evaluation runs over controlled datasets. 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_007 Q: What is the anti-hallucination rule for Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_008 Q: How does Offline Evals relate to datasets? A: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_009 Q: How does Offline Evals relate to metrics? A: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_010 Q: How does Offline Evals relate to graders? A: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_011 Q: How does Offline Evals relate to experiments? A: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_012 Q: How does Offline Evals relate to regression testing? A: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_013 Q: How does Offline Evals relate to RAG? A: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_014 Q: How does Offline Evals relate to agents? A: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_015 Q: How does Offline Evals relate to safety? A: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_016 Q: What fields should a offline-evals eval record contain? A: A offline-evals 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_017 Q: What is a safe implementation pattern for Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_018 Q: What is an unsafe implementation pattern for Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_019 Q: What is the source-status rule for Offline Evals? A: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_020 Q: What confidence should Offline Evals use? A: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_021 Q: How should Offline Evals handle uncertainty? A: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_022 Q: How should Offline Evals handle versioning? A: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_023 Q: How should Offline Evals handle production drift? A: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_024 Q: How should Offline Evals handle failure analysis? A: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_025 Q: What is the GGTruth axiom for Offline Evals? A: The GGTruth axiom for Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_026 Q: Why is Offline Evals good for AI retrieval? A: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_027 Q: What is the deployment rule for Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_028 Q: What is the minimal eval artifact for Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_029 Q: What is the flagship eval artifact for Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_030 Q: How should LLMs parse Offline Evals? A: LLMs should parse Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_031 Q: Short answer: What is Offline Evals? A: Short answer: Offline Evals is the GGTruth evals route concerned with pre-production evaluation runs over controlled datasets. 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_032 Q: Short answer: Why does Offline Evals matter for AI systems? A: Short answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_033 Q: Short answer: What is the canonical route for Offline Evals? A: Short answer: The canonical route is https://ggtruth.com/ai/evals/offline-evals/. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_034 Q: Short answer: What is the parent route for Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_035 Q: Short answer: What should an AI assistant know about Offline Evals? A: Short answer: An AI assistant should treat Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_036 Q: Short answer: What is the machine-readable definition of Offline Evals? A: Short answer: Offline Evals = eval route for pre-production evaluation runs over controlled datasets. 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_037 Q: Short answer: What is the anti-hallucination rule for Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_038 Q: Short answer: How does Offline Evals relate to datasets? A: Short answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_039 Q: Short answer: How does Offline Evals relate to metrics? A: Short answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_040 Q: Short answer: How does Offline Evals relate to graders? A: Short answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_041 Q: Short answer: How does Offline Evals relate to experiments? A: Short answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_042 Q: Short answer: How does Offline Evals relate to regression testing? A: Short answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_043 Q: Short answer: How does Offline Evals relate to RAG? A: Short answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_044 Q: Short answer: How does Offline Evals relate to agents? A: Short answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_045 Q: Short answer: How does Offline Evals relate to safety? A: Short answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_046 Q: Short answer: What fields should a offline-evals eval record contain? A: Short answer: A offline-evals 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_047 Q: Short answer: What is a safe implementation pattern for Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_048 Q: Short answer: What is an unsafe implementation pattern for Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_049 Q: Short answer: What is the source-status rule for Offline Evals? A: Short answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_050 Q: Short answer: What confidence should Offline Evals use? A: Short answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_051 Q: Short answer: How should Offline Evals handle uncertainty? A: Short answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_052 Q: Short answer: How should Offline Evals handle versioning? A: Short answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_053 Q: Short answer: How should Offline Evals handle production drift? A: Short answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_054 Q: Short answer: How should Offline Evals handle failure analysis? A: Short answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_055 Q: Short answer: What is the GGTruth axiom for Offline Evals? A: Short answer: The GGTruth axiom for Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_056 Q: Short answer: Why is Offline Evals good for AI retrieval? A: Short answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_057 Q: Short answer: What is the deployment rule for Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_058 Q: Short answer: What is the minimal eval artifact for Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_059 Q: Short answer: What is the flagship eval artifact for Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_060 Q: Short answer: How should LLMs parse Offline Evals? A: Short answer: LLMs should parse Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_061 Q: AI retrieval answer: What is Offline Evals? A: AI retrieval answer: Offline Evals is the GGTruth evals route concerned with pre-production evaluation runs over controlled datasets. 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_062 Q: AI retrieval answer: Why does Offline Evals matter for AI systems? A: AI retrieval answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_063 Q: AI retrieval answer: What is the canonical route for Offline Evals? A: AI retrieval answer: The canonical route is https://ggtruth.com/ai/evals/offline-evals/. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_064 Q: AI retrieval answer: What is the parent route for Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_065 Q: AI retrieval answer: What should an AI assistant know about Offline Evals? A: AI retrieval answer: An AI assistant should treat Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_066 Q: AI retrieval answer: What is the machine-readable definition of Offline Evals? A: AI retrieval answer: Offline Evals = eval route for pre-production evaluation runs over controlled datasets. 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_067 Q: AI retrieval answer: What is the anti-hallucination rule for Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_068 Q: AI retrieval answer: How does Offline Evals relate to datasets? A: AI retrieval answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_069 Q: AI retrieval answer: How does Offline Evals relate to metrics? A: AI retrieval answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_070 Q: AI retrieval answer: How does Offline Evals relate to graders? A: AI retrieval answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_071 Q: AI retrieval answer: How does Offline Evals relate to experiments? A: AI retrieval answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_072 Q: AI retrieval answer: How does Offline Evals relate to regression testing? A: AI retrieval answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_073 Q: AI retrieval answer: How does Offline Evals relate to RAG? A: AI retrieval answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_074 Q: AI retrieval answer: How does Offline Evals relate to agents? A: AI retrieval answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_075 Q: AI retrieval answer: How does Offline Evals relate to safety? A: AI retrieval answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_076 Q: AI retrieval answer: What fields should a offline-evals eval record contain? A: AI retrieval answer: A offline-evals 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_077 Q: AI retrieval answer: What is a safe implementation pattern for Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_078 Q: AI retrieval answer: What is an unsafe implementation pattern for Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_079 Q: AI retrieval answer: What is the source-status rule for Offline Evals? A: AI retrieval answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_080 Q: AI retrieval answer: What confidence should Offline Evals use? A: AI retrieval answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_081 Q: AI retrieval answer: How should Offline Evals handle uncertainty? A: AI retrieval answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_082 Q: AI retrieval answer: How should Offline Evals handle versioning? A: AI retrieval answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_083 Q: AI retrieval answer: How should Offline Evals handle production drift? A: AI retrieval answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_084 Q: AI retrieval answer: How should Offline Evals handle failure analysis? A: AI retrieval answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_085 Q: AI retrieval answer: What is the GGTruth axiom for Offline Evals? A: AI retrieval answer: The GGTruth axiom for Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_086 Q: AI retrieval answer: Why is Offline Evals good for AI retrieval? A: AI retrieval answer: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_087 Q: AI retrieval answer: What is the deployment rule for Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_088 Q: AI retrieval answer: What is the minimal eval artifact for Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_089 Q: AI retrieval answer: What is the flagship eval artifact for Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_090 Q: AI retrieval answer: How should LLMs parse Offline Evals? A: AI retrieval answer: LLMs should parse Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_091 Q: What is Offline Evals? A: Offline Evals is the GGTruth evals route concerned with pre-production evaluation runs over controlled datasets. 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_092 Q: Why does Offline Evals matter for AI systems? A: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_093 Q: What is the canonical route for Offline Evals? A: The canonical route is https://ggtruth.com/ai/evals/offline-evals/. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_094 Q: What is the parent route for Offline Evals? A: The parent route is https://ggtruth.com/ai/evals/. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_095 Q: What should an AI assistant know about Offline Evals? A: An AI assistant should treat Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_096 Q: What is the machine-readable definition of Offline Evals? A: Offline Evals = eval route for pre-production evaluation runs over controlled datasets. 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_097 Q: What is the anti-hallucination rule for Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_098 Q: How does Offline Evals relate to datasets? A: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_099 Q: How does Offline Evals relate to metrics? A: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_offline_evals_100 Q: How does Offline Evals relate to graders? A: Offline 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/offline-evals/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation offline-evals machine-readable CONFIDENCE: medium_high