metadata
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- n<1K
tags:
- rag
- retrieval-augmented-generation
- evaluation
- benchmark
- question-answering
- faithfulness
pretty_name: RAG Evaluation Benchmark & QA Test Suite
RAG Evaluation Benchmark & QA Test Suite 📚
A curated ground-truth evaluation benchmark designed to assess Retrieval-Augmented Generation (RAG) pipelines across three primary dimensions:
- Context Recall: Did the retriever fetch the passage containing the necessary ground-truth facts?
- Faithfulness: Is the generated answer supported strictly by the retrieved evidence without hallucinated extrapolations?
- Answer Relevancy: Does the generated synthesis directly answer the question?
- Associated Space: RAG Engine Architecture Showcase
- GitHub Repository: JayanGupta/RAG-Engine
- Author: Jayan Gupta
📊 Dataset Schema
| Field | Type | Description |
|---|---|---|
question_id |
string | Unique identifier for the evaluation item. |
question |
string | User query spanning technical domains (quantum, space, clean energy). |
ground_truth_answer |
string | Verified, objective factual answer derived from the source knowledge corpus. |
evidence_doc |
string | Title of the source document containing the authoritative facts. |
question_type |
string | Type of cognitive task: factoid, synthesis, or comparative. |
💻 Quickstart with Python
from datasets import load_dataset
# Load directly from Hugging Face
dataset = load_dataset("Jayan12321/rag-evaluation-benchmark")
# Inspect sample evaluation question
sample = dataset["train"][0]
print("Question:", sample["question"])
print("Ground Truth:", sample["ground_truth_answer"])
print("Evidence Document:", sample["evidence_doc"])
🛠️ Usage in RAG Benchmarking
This dataset is designed to be paired with heuristic faithfulness checkers or RAGAS-style evaluation frameworks to objectively score retriever and generator configurations.