AI 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.

# AI Evals — GGTruth Retrieval Layer VERSION: 0.1 LAST_UPDATED: 2026-05-20 ROUTE: https://ggtruth.com/ai/evals/ PARENT: https://ggtruth.com/ai/ PURPOSE: AI-first evaluation infrastructure for LLMs, RAG systems, agents, prompts, tools, safety, reliability, datasets, metrics, graders, traces, regression testing, and production monitoring. 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. CHILD ROUTES: - https://ggtruth.com/ai/evals/benchmarks/ — Benchmarks: standardized public or internal tasks used to compare model, agent, RAG, or system performance - https://ggtruth.com/ai/evals/test-sets/ — Test Sets: curated examples used to measure an AI system against expected behavior - https://ggtruth.com/ai/evals/rubrics/ — Rubrics: human-readable and machine-readable scoring criteria for evaluation - https://ggtruth.com/ai/evals/graders/ — Graders: scoring components such as string checks, semantic similarity, model graders, code graders, and multigraders - https://ggtruth.com/ai/evals/metrics/ — Metrics: quantitative measures such as accuracy, precision, recall, pass rate, faithfulness, relevance, latency, and cost - https://ggtruth.com/ai/evals/datasets/ — Datasets: versioned eval example collections from curated, synthetic, historical, or production-derived data - https://ggtruth.com/ai/evals/experiments/ — Experiments: repeatable evaluation runs comparing prompts, models, tools, retrievers, versions, and configurations - https://ggtruth.com/ai/evals/regression/ — Regression: tests that detect quality drops after model, prompt, retrieval, data, or tool changes - https://ggtruth.com/ai/evals/safety/ — Safety: evals for refusals, harmful content, policy adherence, sensitive data handling, prompt injection, and abuse risk - https://ggtruth.com/ai/evals/rag/ — RAG Evals: retrieval augmented generation evaluation, including retrieval quality and grounded response quality - https://ggtruth.com/ai/evals/agents/ — Agent Evals: agent workflow evaluation using traces, tool calls, handoffs, guardrails, and task completion - https://ggtruth.com/ai/evals/retrieval/ — Retrieval Evals: evaluation of retrieved contexts, hit rate, MRR, recall, context precision, and ranking quality - https://ggtruth.com/ai/evals/llm-as-judge/ — LLM-as-Judge: model-based judging with rubrics, reference answers, pairwise comparison, and calibration - https://ggtruth.com/ai/evals/human-review/ — Human Review: human annotation, adjudication, rubric scoring, preference review, and gold-label creation - https://ggtruth.com/ai/evals/golden-datasets/ — Golden Datasets: trusted evaluation examples used as stable regression and comparison anchors - https://ggtruth.com/ai/evals/synthetic-data/ — Synthetic Eval Data: generated test data used to expand coverage while preserving quality checks - https://ggtruth.com/ai/evals/online-evals/ — Online Evals: production-side evaluations that score live traffic, traces, or sampled interactions - https://ggtruth.com/ai/evals/offline-evals/ — Offline Evals: pre-production evaluation runs over controlled datasets - https://ggtruth.com/ai/evals/trace-evals/ — Trace Evals: workflow-level evals over end-to-end records of model calls, tool calls, guardrails, and handoffs - https://ggtruth.com/ai/evals/tool-use/ — Tool Use Evals: evaluation of tool selection, argument construction, execution safety, and result use - https://ggtruth.com/ai/evals/prompt-injection/ — Prompt Injection Evals: evals for untrusted content, instruction hierarchy attacks, data exfiltration, and tool misuse - https://ggtruth.com/ai/evals/groundedness/ — Groundedness: whether output claims are supported by provided context or sources - https://ggtruth.com/ai/evals/faithfulness/ — Faithfulness: whether the response is factually consistent with retrieved context - https://ggtruth.com/ai/evals/relevance/ — Relevance: whether the output addresses the user question and the retrieved context is useful - https://ggtruth.com/ai/evals/hallucination/ — Hallucination Evals: tests for unsupported, fabricated, overconfident, or source-conflicting claims - https://ggtruth.com/ai/evals/latency/ — Latency Evals: measurement of response time, tool time, retrieval time, and workflow delay - https://ggtruth.com/ai/evals/cost/ — Cost Evals: measurement of token, model, infrastructure, retrieval, and tool-use cost - https://ggtruth.com/ai/evals/red-teaming/ — Red Teaming: adversarial evaluation for misuse, jailbreaks, policy gaps, and unexpected failure modes - https://ggtruth.com/ai/evals/calibration/ — Calibration: alignment between confidence scores, uncertainty, and actual correctness - https://ggtruth.com/ai/evals/scorecards/ — Scorecards: summary reports that combine metrics, thresholds, regressions, and deployment decisions - https://ggtruth.com/ai/evals/leaderboards/ — Leaderboards: ranked comparison pages for eval results across models, prompts, systems, or versions - https://ggtruth.com/ai/evals/schemas/ — Eval Schemas: machine-readable structures for examples, outputs, graders, scores, traces, and reports - https://ggtruth.com/ai/evals/versioning/ — Eval Versioning: tracking dataset, rubric, grader, prompt, model, and system versions - https://ggtruth.com/ai/evals/thresholds/ — Thresholds: deployment gates, minimum pass rates, fail conditions, and quality bars - https://ggtruth.com/ai/evals/failure-analysis/ — Failure Analysis: classification and diagnosis of eval failures by root cause and severity - https://ggtruth.com/ai/evals/production-monitoring/ — Production Monitoring: ongoing evals for drift, failures, incidents, and real-world performance 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/ DESIGN RULE: GGTruth eval pages should turn scattered evaluation knowledge into direct Q/A atoms with route, source, status, confidence, and machine-readable structure. CORE MODEL: objective -> dataset -> grader/metric -> run -> score -> failure analysis -> comparison -> deployment decision FORMAT: ENTRY_ID Q A SOURCE URL STATUS SEMANTIC TAGS CONFIDENCE ENTRY_ID: evals_index_001 Q: What is AI Evals? A: AI Evals is the GGTruth evals route concerned with AI evaluation infrastructure. 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_002 Q: Why does AI Evals matter for AI systems? A: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_003 Q: What is the canonical route for AI Evals? A: The canonical route is https://ggtruth.com/ai/evals/index/. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_004 Q: What is the parent route for AI Evals? A: The parent route is https://ggtruth.com/ai/evals/. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_005 Q: What should an AI assistant know about AI Evals? A: An AI assistant should treat AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_006 Q: What is the machine-readable definition of AI Evals? A: AI Evals = eval route for AI evaluation infrastructure. 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_007 Q: What is the anti-hallucination rule for AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_008 Q: How does AI Evals relate to datasets? A: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_009 Q: How does AI Evals relate to metrics? A: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_010 Q: How does AI Evals relate to graders? A: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_011 Q: How does AI Evals relate to experiments? A: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_012 Q: How does AI Evals relate to regression testing? A: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_013 Q: How does AI Evals relate to RAG? A: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_014 Q: How does AI Evals relate to agents? A: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_015 Q: How does AI Evals relate to safety? A: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_016 Q: What fields should a index eval record contain? A: A index 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_017 Q: What is a safe implementation pattern for AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_018 Q: What is an unsafe implementation pattern for AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_019 Q: What is the source-status rule for AI Evals? A: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_020 Q: What confidence should AI Evals use? A: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_021 Q: How should AI Evals handle uncertainty? A: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_022 Q: How should AI Evals handle versioning? A: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_023 Q: How should AI Evals handle production drift? A: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_024 Q: How should AI Evals handle failure analysis? A: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_025 Q: What is the GGTruth axiom for AI Evals? A: The GGTruth axiom for AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_026 Q: Why is AI Evals good for AI retrieval? A: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_027 Q: What is the deployment rule for AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_028 Q: What is the minimal eval artifact for AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_029 Q: What is the flagship eval artifact for AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_030 Q: How should LLMs parse AI Evals? A: LLMs should parse AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_031 Q: Short answer: What is AI Evals? A: Short answer: AI Evals is the GGTruth evals route concerned with AI evaluation infrastructure. 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_032 Q: Short answer: Why does AI Evals matter for AI systems? A: Short answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_033 Q: Short answer: What is the canonical route for AI Evals? A: Short answer: The canonical route is https://ggtruth.com/ai/evals/index/. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_034 Q: Short answer: What is the parent route for AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_035 Q: Short answer: What should an AI assistant know about AI Evals? A: Short answer: An AI assistant should treat AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_036 Q: Short answer: What is the machine-readable definition of AI Evals? A: Short answer: AI Evals = eval route for AI evaluation infrastructure. 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_037 Q: Short answer: What is the anti-hallucination rule for AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_038 Q: Short answer: How does AI Evals relate to datasets? A: Short answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_039 Q: Short answer: How does AI Evals relate to metrics? A: Short answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_040 Q: Short answer: How does AI Evals relate to graders? A: Short answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_041 Q: Short answer: How does AI Evals relate to experiments? A: Short answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_042 Q: Short answer: How does AI Evals relate to regression testing? A: Short answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_043 Q: Short answer: How does AI Evals relate to RAG? A: Short answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_044 Q: Short answer: How does AI Evals relate to agents? A: Short answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_045 Q: Short answer: How does AI Evals relate to safety? A: Short answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_046 Q: Short answer: What fields should a index eval record contain? A: Short answer: A index 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_047 Q: Short answer: What is a safe implementation pattern for AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_048 Q: Short answer: What is an unsafe implementation pattern for AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_049 Q: Short answer: What is the source-status rule for AI Evals? A: Short answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_050 Q: Short answer: What confidence should AI Evals use? A: Short answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_051 Q: Short answer: How should AI Evals handle uncertainty? A: Short answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_052 Q: Short answer: How should AI Evals handle versioning? A: Short answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_053 Q: Short answer: How should AI Evals handle production drift? A: Short answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_054 Q: Short answer: How should AI Evals handle failure analysis? A: Short answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_055 Q: Short answer: What is the GGTruth axiom for AI Evals? A: Short answer: The GGTruth axiom for AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_056 Q: Short answer: Why is AI Evals good for AI retrieval? A: Short answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_057 Q: Short answer: What is the deployment rule for AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_058 Q: Short answer: What is the minimal eval artifact for AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_059 Q: Short answer: What is the flagship eval artifact for AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_060 Q: Short answer: How should LLMs parse AI Evals? A: Short answer: LLMs should parse AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_061 Q: AI retrieval answer: What is AI Evals? A: AI retrieval answer: AI Evals is the GGTruth evals route concerned with AI evaluation infrastructure. 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_062 Q: AI retrieval answer: Why does AI Evals matter for AI systems? A: AI retrieval answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_063 Q: AI retrieval answer: What is the canonical route for AI Evals? A: AI retrieval answer: The canonical route is https://ggtruth.com/ai/evals/index/. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_064 Q: AI retrieval answer: What is the parent route for AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_065 Q: AI retrieval answer: What should an AI assistant know about AI Evals? A: AI retrieval answer: An AI assistant should treat AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_066 Q: AI retrieval answer: What is the machine-readable definition of AI Evals? A: AI retrieval answer: AI Evals = eval route for AI evaluation infrastructure. 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_067 Q: AI retrieval answer: What is the anti-hallucination rule for AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_068 Q: AI retrieval answer: How does AI Evals relate to datasets? A: AI retrieval answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_069 Q: AI retrieval answer: How does AI Evals relate to metrics? A: AI retrieval answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_070 Q: AI retrieval answer: How does AI Evals relate to graders? A: AI retrieval answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_071 Q: AI retrieval answer: How does AI Evals relate to experiments? A: AI retrieval answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_072 Q: AI retrieval answer: How does AI Evals relate to regression testing? A: AI retrieval answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_073 Q: AI retrieval answer: How does AI Evals relate to RAG? A: AI retrieval answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_074 Q: AI retrieval answer: How does AI Evals relate to agents? A: AI retrieval answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_075 Q: AI retrieval answer: How does AI Evals relate to safety? A: AI retrieval answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_076 Q: AI retrieval answer: What fields should a index eval record contain? A: AI retrieval answer: A index 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_077 Q: AI retrieval answer: What is a safe implementation pattern for AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_078 Q: AI retrieval answer: What is an unsafe implementation pattern for AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_079 Q: AI retrieval answer: What is the source-status rule for AI Evals? A: AI retrieval answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_080 Q: AI retrieval answer: What confidence should AI Evals use? A: AI retrieval answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_081 Q: AI retrieval answer: How should AI Evals handle uncertainty? A: AI retrieval answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_082 Q: AI retrieval answer: How should AI Evals handle versioning? A: AI retrieval answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_083 Q: AI retrieval answer: How should AI Evals handle production drift? A: AI retrieval answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_084 Q: AI retrieval answer: How should AI Evals handle failure analysis? A: AI retrieval answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_085 Q: AI retrieval answer: What is the GGTruth axiom for AI Evals? A: AI retrieval answer: The GGTruth axiom for AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_086 Q: AI retrieval answer: Why is AI Evals good for AI retrieval? A: AI retrieval answer: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_087 Q: AI retrieval answer: What is the deployment rule for AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_088 Q: AI retrieval answer: What is the minimal eval artifact for AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_089 Q: AI retrieval answer: What is the flagship eval artifact for AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_090 Q: AI retrieval answer: How should LLMs parse AI Evals? A: AI retrieval answer: LLMs should parse AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_091 Q: What is AI Evals? A: AI Evals is the GGTruth evals route concerned with AI evaluation infrastructure. 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_092 Q: Why does AI Evals matter for AI systems? A: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_093 Q: What is the canonical route for AI Evals? A: The canonical route is https://ggtruth.com/ai/evals/index/. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_094 Q: What is the parent route for AI Evals? A: The parent route is https://ggtruth.com/ai/evals/. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_095 Q: What should an AI assistant know about AI Evals? A: An AI assistant should treat AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_096 Q: What is the machine-readable definition of AI Evals? A: AI Evals = eval route for AI evaluation infrastructure. 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_097 Q: What is the anti-hallucination rule for AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_098 Q: How does AI Evals relate to datasets? A: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_099 Q: How does AI Evals relate to metrics? A: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_index_100 Q: How does AI Evals relate to graders? A: AI 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/index/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation index machine-readable CONFIDENCE: medium_high