Applied RAG & Natural Language Processing
Collection
End-to-end Retrieval-Augmented Generation architectures, benchmark evaluation datasets, and sequence classification pipelines. • 4 items • Updated
question_id stringclasses 10
values | question stringclasses 10
values | ground_truth_answer stringclasses 10
values | evidence_doc stringclasses 4
values | question_type stringclasses 3
values |
|---|---|---|---|---|
qa_001 | What temperature must superconducting qubits be cooled down to? | Superconducting qubits must typically be cooled down to approximately 15 millikelvin (-273.135 degrees Celsius) using dilution refrigerators. | Quantum Computing Fundamentals | factoid |
qa_002 | What quantum hardware architectures exist besides superconducting circuits? | Trapped-ion systems (used by IonQ and Quantinuum) and photonic quantum computers. | Quantum Computing Fundamentals | synthesis |
qa_003 | What is the primary mirror of the James Webb Space Telescope composed of? | The 6.5-meter diameter primary mirror is composed of 18 hexagonal segments made from gold-plated beryllium. | James Webb Space Telescope Overview | factoid |
qa_004 | Where is the James Webb Space Telescope stationed in space? | It is stationed at the Sun-Earth Second Lagrange Point (L2), approximately 1.5 million kilometers from Earth. | James Webb Space Telescope Overview | factoid |
qa_005 | What are the four primary scientific instruments onboard JWST? | NIRCam (Near-Infrared Camera), NIRSpec (Near-Infrared Spectrograph), MIRI (Mid-Infrared Instrument), and FGS/NIRISS (Fine Guidance Sensor and Near-Infrared Imager and Slitless Spectrograph). | James Webb Space Telescope Overview | factoid |
qa_006 | What battery chemistries are typically deployed for short-duration grid storage? | Lithium iron phosphate (LFP) or nickel manganese cobalt (NMC) chemistries providing 2 to 4 hours of storage. | Renewable Energy and Modern Grid Storage | factoid |
qa_007 | Which technology accounts for over 90% of global operational energy storage capacity? | Pumped-storage hydroelectricity. | Renewable Energy and Modern Grid Storage | factoid |
qa_008 | How does green hydrogen contribute to seasonal renewable energy storage? | Green hydrogen is produced through water electrolysis powered by surplus renewable electricity and can be stored for seasonal demand and heavy industrial decarbonization. | Renewable Energy and Modern Grid Storage | synthesis |
qa_009 | Compare the cooling requirements of superconducting quantum processors with JWST instruments. | Superconducting qubits require ultra-cryogenic dilution refrigeration to 15 millikelvin (-273.135 °C), whereas JWST instruments operate below 50 Kelvin (-223 °C) utilizing a passive 5-layer sunshield and onboard cryocoolers. | Multi-Document Synthesis (Quantum + JWST) | comparative |
qa_010 | What algorithm threatens legacy cryptographic standards if fault-tolerant quantum computing is achieved? | Shor's algorithm. | Quantum Computing Fundamentals | factoid |
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.
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