RAG Evaluation Datasets for Retrieval-Augmented AI
Domain-specific question-context-answer triples with retrieval labels, answer quality scores, and faithfulness annotations. Built for evaluating and improving production RAG pipelines.
20+
Years in AI training data
1,000+
Language locales
1M+
Hours of video annotated
ISO
27001 certified
The Challenge
Beyond Open-Domain QA Benchmarks
BEIR, HotpotQA, and Natural Questions benchmark general-domain retrieval and question answering. Evaluating production RAG systems over proprietary document corpora requires custom evaluation datasets built from your own knowledge base.
A RAG system's quality depends on retrieval accuracy over your specific document collection and answer faithfulness to retrieved context. Generic benchmarks cannot measure these properties over your enterprise knowledge base, product documentation, or domain-specific corpus.
Off-the-shelf RAG benchmarks suffer from corpus mismatch (public Wikipedia or web corpora differ from enterprise knowledge bases) and answer style gaps: benchmark answers follow QA conventions that differ from your product's expected response format.
LXT builds custom RAG evaluation datasets over your document corpus. We create question-context-answer triples with retrieval relevance labels and answer faithfulness annotations that let you measure and improve your RAG pipeline's actual performance.
Why Teams Upgrade
Limitations of Public RAG Evaluation Datasets
Standard benchmarks serve research well. Production deployments need more.
| Dataset | Primary Limitation | Impact |
|---|---|---|
| BEIR Benchmark | Public web and Wikipedia corpora only; cannot evaluate retrieval quality over proprietary enterprise documents | Corpus mismatch |
| HotpotQA | Multi-hop Wikipedia questions; answer style and complexity does not match enterprise or domain-specific QA | Wikipedia-only |
| Natural Questions | Google search queries against Wikipedia; web search distribution differs from enterprise knowledge base query patterns | Web distribution |
| TriviaQA | Trivia knowledge questions; evaluates factual recall rather than enterprise document retrieval and synthesis | Trivia-only |
| MS MARCO | Bing query logs against web documents; consumer search patterns differ from enterprise knowledge queries | Consumer search |
Not sure which specs you need?
Our data specialists help you scope the right dataset for your model architecture.
Configurable Specifications
Specs Built Around Your Model
Public datasets come fixed. Yours is configured for your architecture, environment, and use case.
Question Types
Query Coverage
- Factoid: Single-answer factual questions with clear correct answers in your corpus
- Multi-Hop: Questions requiring synthesis across multiple retrieved documents
- Unanswerable: Questions where correct answer is not in the corpus for abstention testing
Annotation Depth
Evaluation Labels
- Retrieval Relevance: Document-level and passage-level relevance judgments per question
- Answer Faithfulness: Is the answer supported by retrieved context? (hallucination detection)
- Answer Completeness: Does the answer fully address the question given retrieved content?
Corpus Alignment
Domain Matching
- Document Types: Your knowledge base, product docs, policy documents, or domain corpus
- Query Distribution: Questions sampled from real user query logs or expert-generated queries
- Difficulty Tiers: Easy, medium, and hard question stratification for diagnostic evaluation
Need a custom configuration?
We've built datasets across dozens of domains and use cases. Let's scope yours.
Capturing Complexity
Edge Cases in RAG Evaluation
High-accuracy models handle rare attributes that public datasets miss.
Conflicting Information Across Documents
Enterprise corpora contain outdated and updated versions of the same information. Conflict detection and temporal priority annotation supports training retrieval systems to handle document version conflicts.
Multi-Hop Reasoning Chains
Some questions require chaining information from two or more retrieved passages. Retrieval chain annotation documents which passages are required and in what order for complex question answering.
Unanswerable and Out-of-Scope Questions
Production RAG systems receive questions outside their knowledge base scope. Unanswerable question annotation with abstention ground truth tests correct refusal behavior.
Format-Specific Answer Requirements
Enterprise questions may require tabular, list, or structured answers from retrieved content. Answer format annotation supports evaluating whether RAG output matches expected response structure.
Ground Truth Quality
Human-in-the-Loop Annotation
Precise annotation bridges raw data and learnable signal. Expert annotators deliver precision automated tools can't match.
Question Generation
Expert annotators generate questions from your document corpus covering key facts, multi-hop reasoning, and edge cases. Questions reflect real user query patterns.
Retrieval Relevance Labels
Passage-level relevance judgments (highly relevant, relevant, not relevant) for each question-document pair in the retrieval candidate pool.
Answer Faithfulness Annotation
Human judges verify whether model-generated answers are supported by retrieved context, identifying hallucinations and faithfulness failures for pipeline improvement.
Industry Applications
RAG Evaluation Datasets for Your Domain
Custom taxonomies and collection protocols for specific deployment contexts.
Enterprise Search
Internal knowledge base QA, employee self-service
Legal AI
Contract QA, regulatory search, legal research
Healthcare AI
Clinical guideline retrieval, drug information QA
Financial AI
Policy document QA, investment research retrieval
Customer Support
Product documentation QA, support knowledge base
EdTech
Course material QA, educational content retrieval
LLM Product Evaluation
RAG pipeline benchmarking and regression testing
Retrieval System Tuning
Embedding model and re-ranker fine-tuning
Compliance & Ethics
Secure and Ethical Data Collection
Data collection involving people and sensitive content requires robust security, compliance, and ethical protocols at every stage.
Global Demographic Reach
Collection across 1,000+ locales and diverse demographics to prevent algorithmic bias in your deployed models.
ISO 27001 Certified
Sensitive projects processed in certified secure facilities meeting the highest information security standards.
GDPR & Privacy Compliance
All collection and annotation protocols vetted for consent and privacy. Legally robust for global deployment.
Frequently Asked Questions
RAG Evaluation Dataset FAQs
Can you build evaluation sets from our document corpus?+
Yes. We generate questions from your proprietary documents and annotate answers with retrieval relevance labels. We operate under a data processing agreement and can work within your secure environment.
How do you generate questions that reflect real user queries?+
We analyze your query logs if available, and supplement with expert-generated questions that cover the key information types in your corpus. Questions are reviewed for naturalness and difficulty balance.
Do you produce retrieval relevance judgments at passage level?+
Yes. We annotate relevance at both document and passage level, producing graded relevance scores (highly relevant, relevant, not relevant) for each question-document pair.
Can you annotate model outputs for faithfulness?+
Yes. We provide human evaluation services to assess model-generated answers for faithfulness to retrieved context. This produces structured hallucination detection reports and per-example faithfulness scores.
What formats does the evaluation dataset use?+
BEIR-compatible JSON, Hugging Face datasets format, and custom formats. Relevance judgments in TREC qrels format for compatibility with standard IR evaluation tools.
What does a custom RAG evaluation dataset cost?+
Projects range from $15K for focused single-domain evaluation sets (500-2,000 questions) to $80K+ for large-scale multi-domain evaluation suites with full retrieval relevance annotation.
Can you help us interpret evaluation results?+
Yes. We provide evaluation analysis reports identifying retrieval failure modes, answer quality patterns, and specific improvement recommendations for your RAG pipeline components.
Get Started
Scope Your Custom RAG Evaluation Dataset
Share your document corpus type, query patterns, and evaluation requirements. A RAG specialist will provide a detailed proposal within 48 hours.
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