How to Structure Your Evaluation Data for Bulletproof RAG Testing

Your Retrieval-Augmented Generation (RAG) pipeline is finally working. It retrieves documents and generates impressive answers in your controlled demos. But how will it perform in the wild? How do you prevent it from hallucinating, providing irrelevant answers, or breaking silently when you tweak a prompt or swap out an LLM?

The answer isn't in more frantic prompt engineering; it's in systematic, data-driven evaluation. The success of your AI experiments and the reliability of your production RAG system hinge on one critical asset: your evaluation dataset. Without a "golden set" to test against, you're flying blind.

This guide provides a comprehensive framework for creating and structuring high-quality evaluation datasets. With this foundation, you can run meaningful A/B tests, validate changes, and optimize your RAG pipelines for relevance, faithfulness, and answer quality with confidence.

Why "Good Enough" Isn't Good Enough for Evaluation Data

In AI development, the "garbage in, garbage out" principle applies as much to evaluation as it does to training. A weak or biased evaluation dataset gives you a false sense of security. You might run an experiment and see a 10% improvement, but if your test data only covers simple, "happy path" questions, that improvement means nothing for the complex, ambiguous queries your users will inevitably ask.

A robust evaluation dataset is your single source of truth. It allows you to:

• Measure Meaningful Metrics: Quantify improvements in relevance, cost, and latency.
• Prevent Regressions: Ensure a change that improves one area doesn't break another.
• Optimize Confidently: Make data-driven decisions about prompts, models, and configurations.
• Automate Testing: Integrate AI validation into your CI/CD pipeline for continuous improvement.

The Anatomy of a Golden RAG Evaluation Dataset

A truly effective evaluation dataset is more than just a list of questions. It's a structured collection of data points, where each point contains the necessary components to rigorously test every stage of your RAG pipeline.

Let's break down the essential components for each entry in your dataset.

1. The question

This is the input query you'll feed into your RAG pipeline.

• What it is: A string representing a user's question or prompt.
• Best Practices:
  • Source from Reality: Pull questions from real user logs, customer support tickets, and sales inquiries. These are your most valuable source of truth.
  • Include Adversarial Examples: Craft questions that are intentionally ambiguous, irrelevant, or designed to test edge cases. What happens when a user asks something completely outside your knowledge base?
  • Ensure Variety: Cover the full spectrum of topics, complexities (from simple fact-finding to complex summarization), and intents your system is expected to handle.

2. The ground_truth_context_ids

This is the heart of RAG evaluation. It defines what "correct" retrieval looks like for a given question.

• What it is: An array of document IDs or chunk IDs that contain the necessary and sufficient information to answer the question.
• Best Practices:
  • Human-Curated: This requires a human to read the question and identify the exact source document chunks that should be retrieved. This is labor-intensive but non-negotiable for accurate relevance measurement.
  • Be Specific: Don't just point to a whole document; identify the specific chunks. This helps you fine-tune your chunking and embedding strategies.
  • This component allows you to calculate Context Precision (Did we retrieve irrelevant docs?) and Context Recall (Did we miss any relevant docs?).

3. The ground_truth_answer

This is the ideal, factually correct answer based only on the ground truth context.

• What it is: A string representing the "perfect" answer.
• Best Practices:
  • Synthesize from Ground Truth Context: The answer should be written as if the ground_truth_context was the only information available in the world. This is crucial for evaluating Faithfulness (Does the LLM hallucinate?).
  • Clear and Concise: The answer should be direct and unambiguous, serving as a clean benchmark for measuring Answer Relevance and semantic correctness against the generated answer.

4. metadata

Metadata allows you to slice and dice your experiment results to uncover hidden insights into your system's performance.

• What it is: A JSON object containing key-value pairs to categorize the data point.
• Best Practices:
  • Categorization: Include fields like category ("Pricing", "Technical Support") or topic.
  • Difficulty: Add a difficulty score ("Easy", "Medium", "Hard") to see if your pipeline struggles with more complex queries.
  • Question Type: Use a type field ("Fact-based", "Summarization", "Comparison") to test different agentic workflow capabilities.

Putting It All Together: A Sample Data Structure

Here’s what a single entry in your RAG evaluation dataset might look like in JSON format:

{
  "question_id": "eval-1a2b3c",
  "question": "What are the latency and cost differences between the v1 and v2 RAG pipelines?",
  "ground_truth_context_ids": [
    "doc_results_v1_chunk_4",
    "doc_results_v2_chunk_7"
  ],
  "ground_truth_answer": "The v2 candidate pipeline demonstrated a lower average latency of 950ms compared to the v1 baseline's 1200ms. It also had a lower cost per query at $0.0021 versus $0.0025 for v1.",
  "metadata": {
    "category": "Performance",
    "difficulty": "Medium",
    "type": "Comparison"
  }
}

From Data to Insights with Experiments.do

Creating this golden dataset is the first, most crucial step. The next is using it to run systematic experiments. Manually testing dozens of questions against multiple pipeline variations is tedious and unscalable. This is where a dedicated AI experimentation platform becomes essential.

With Experiments.do, you can operationalize your evaluation dataset to drive continuous improvement.

1. Define Your Experiment: Set up an A/B test comparing your current RAG pipeline (rag-v1_baseline) against a new candidate with a different prompt or LLM (rag-v2_candidate).
2. Run with Your Data: Feed your entire evaluation dataset into the experiment. Experiments.do automates the process of running each question through both variants.
3. Analyze the Results: The platform measures key metrics for each variant, comparing the outputs against your ground truth data. It automatically calculates relevance_score, faithfulness, and other custom metrics you define, like latency and cost.
4. Declare a Winner: Get a clear, data-backed verdict on which configuration performs best across your entire test suite.

The output looks clean, simple, and actionable—just like the code on our homepage:

{
  "experimentId": "exp-1a2b3c4d5e",
  "name": "RAG Pipeline Performance Test",
  "status": "completed",
  "winner": "rag-v2",
  "results": [
    {
      "variantId": "rag-v1_baseline",
      "metrics": { "relevance_score": 0.88, "latency_ms_avg": 1200, "cost_per_query": 0.0025 }
    },
    {
      "variantId": "rag-v2_candidate",
      "metrics": { "relevance_score": 0.95, "latency_ms_avg": 950, "cost_per_query": 0.0021 }
    }
  ]
}

Because Experiments.do is API-first, you can trigger these evaluations directly from your CI/CD pipeline, turning every commit into an opportunity to validate and improve your AI.

Stop guessing and start building bulletproof RAG systems. Invest in your evaluation data, and you'll invest in the quality and reliability of your AI services.

Ready to move from anecdotal checks to rigorous AI validation? Explore Experiments.do and ship your next AI service with confidence.