Electrical Engineering AI Evaluation Benchmark

A Python-based benchmarking framework for evaluating AI-generated responses on Electrical Engineering tasks.

This project provides a structured methodology for assessing technical accuracy, semantic similarity, factual consistency, and hallucination behavior in AI-generated answers across multiple Electrical Engineering domains.

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Overview

Large Language Models (LLMs) are increasingly used to assist engineers in technical problem-solving and knowledge retrieval. However, evaluating the reliability of AI-generated engineering responses requires domain-specific benchmarks.

This project implements an evaluation pipeline that measures:

• Technical Accuracy
• Semantic Similarity
• Hallucination Detection
• Domain-Level Performance
• Benchmark Visualization

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Evaluation Domains

The benchmark includes Electrical Engineering questions from multiple technical areas:

• Transformers
• Power Systems
• Electrical Machines
• Protection Systems
• Power Distribution
• Fault Analysis
• Circuit Analysis

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Project Structure

electrical-engineering-ai-evaluation-benchmark/
│
├── data/
│   ├── raw/
│   │   ├── electrical_qa_dataset.csv
│   │   ├── ground_truth_answers.csv
│   │   └── ai_generated_answers.csv
│   │
│   ├── processed/
│   │   ├── benchmark_dataset.csv
│   │   └── evaluation_results.csv
│   │
│   └── sample/
│       ├── sample_questions.csv
│       └── sample_evaluation.csv
│
├── src/
│   ├── evaluation/
│   ├── visualization/
│   └── utils/
│
├── results/
│   └── figures/
│       ├── accuracy_distribution.png
│       ├── benchmark_overview.png
│       ├── domain_performance.png
│       ├── hallucination_distribution.png
│       └── hallucination_rate.png
│
├── requirements.txt
├── LICENSE
└── README.md

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Evaluation Metrics

Technical Accuracy Score

Measures engineering correctness on a scale of:

Score	Description
5	Completely correct
4	Minor technical issues
3	Partially correct
2	Significant errors
1	Incorrect answer

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Similarity Score

Measures semantic similarity between:

• Ground Truth Answer
• AI Generated Answer

Range:

0.00 - 1.00

Higher values indicate stronger alignment.

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Hallucination Analysis

The benchmark identifies:

• Unsupported technical claims
• Fabricated engineering facts
• Incorrect calculations
• Invalid technical references

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Visualization Outputs

Accuracy Distribution

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Benchmark Overview

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Domain Performance

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Hallucination Distribution

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Hallucination Rate

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Installation

Clone the repository:

git clone https://github.com/yourusername/electrical-engineering-ai-evaluation-benchmark.git

cd electrical-engineering-ai-evaluation-benchmark

Create virtual environment:

python -m venv venv

Activate environment:

Windows

venv\Scripts\activate

Linux / macOS

source venv/bin/activate

Install dependencies:

pip install -r requirements.txt

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Usage

Generate benchmark visualizations:

python src/visualization/score_distribution.py

python src/visualization/hallucination_charts.py

python src/visualization/benchmark_dashboard.py

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Sample Evaluation Output

Question ID	Model	Category	Accuracy	Similarity
1	GPT_Model_A	Transformer	5	0.94
2	GPT_Model_A	Transformer	5	0.96
3	GPT_Model_A	Transformer	5	0.92

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Applications

This benchmark framework can be used for:

• LLM Evaluation
• Engineering AI Auditing
• Technical Response Validation
• Hallucination Detection Research
• AI Quality Assurance
• Domain-Specific Benchmarking

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Future Improvements

Planned enhancements include:

• Multi-model comparison
• Automated fact verification
• RAG-based evaluation
• Statistical significance testing
• Expanded engineering datasets
• Interactive dashboards

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Technologies

• Python
• Pandas
• NumPy
• Matplotlib
• Scikit-learn

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License

This project is released under the MIT License.

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Author

Electrical Engineering & AI Evaluation Portfolio Project

Focused on evaluating the reliability and technical correctness of AI systems in engineering applications.