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generate-rag-dataset

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Generate a synthetic evaluation dataset from your RAG knowledge base. Creates diverse Q&A pairs with expected answers and relevant context, ready for LangWatch experiments and platform import. Use when you need test data for your RAG pipeline.

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How to use this skill

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Generate a RAG Evaluation Dataset

This recipe analyzes your RAG knowledge base and generates a comprehensive Q&A evaluation dataset.

Step 1: Analyze the Knowledge Base

Read the codebase to find the knowledge base:

  • Document files (PDFs, markdown, text files)
  • Database schemas (if documents are stored in a DB)
  • Vector store configuration (what's being embedded)
  • Chunking strategy (how documents are split)

Read every document you can access. Understand:

  • What topics does the knowledge base cover?
  • What's the depth of information?
  • What terminology is used?
  • What are the boundaries (what's NOT covered)?

Step 2: Generate Diverse Question Types

Create questions across these categories:

Factual Recall

Direct questions answerable from a single passage:

  • "What is the recommended threshold for X?"
  • "When should Y be applied?"

Multi-Hop Reasoning

Questions requiring information from multiple passages:

  • "Given condition A and condition B, what should be done?"
  • "How do X and Y interact when Z occurs?"

Comparison

Questions comparing concepts within the knowledge base:

  • "What's the difference between approach A and approach B?"
  • "When should you use X instead of Y?"

Edge Cases

Questions about boundary conditions or unusual scenarios:

  • "What happens if the measurement is outside normal range?"
  • "What if two recommendations conflict?"

Negative Cases

Questions about topics NOT covered by the knowledge base:

  • "Does the system support Z?" (when it doesn't)
  • Questions requiring external knowledge the KB doesn't have

These help test that the agent correctly says "I don't know" rather than hallucinating.

Step 3: Include Context Per Row

For each Q&A pair, include the relevant document chunk(s) that contain the answer. This enables:

  • Platform experiments without the full RAG pipeline
  • Evaluating answer quality independent of retrieval quality
  • Testing with different prompts using the same retrieved context

Format:

{
    "input": "When should I irrigate apple orchards?",
    "expected_output": "Irrigate when soil moisture exceeds 35 kPa...",
    "context": "## Irrigation Management\nSoil moisture threshold for apple orchards: maintain between 25-35 kPa...",
    "question_type": "factual_recall"
}

Step 4: Export Formats

Create both:

Python DataFrame (for SDK experiments)

import pandas as pd
df = pd.DataFrame(dataset)
df.to_csv("rag_evaluation_dataset.csv", index=False)

Platform-Ready CSV

Export with columns: input, expected_output, context, question_type This can be imported directly into LangWatch platform datasets.

Step 5: Validate Dataset Quality

Before using the dataset:

  1. Check topic coverage — are all knowledge base topics represented?
  2. Verify answers are actually in the context — no hallucinated expected outputs
  3. Check question diversity — not all the same type
  4. Verify negative cases have appropriate "I don't know" expected outputs
  5. Run a quick experiment to baseline accuracy

Common Mistakes

  • Do NOT generate questions without reading the actual knowledge base first
  • Do NOT skip negative cases — testing "I don't know" is crucial for RAG
  • Do NOT use the same question pattern for every entry — diversify types
  • Do NOT forget to include the relevant context per row
  • Do NOT generate expected outputs that aren't actually in the knowledge base