EvalGuard is a structured evaluation and benchmarking system for Large Language Model (LLM)-based Question Answering systems, including RAG (Retrieval-Augmented Generation) and chatbot pipelines.
It combines LLM-as-a-judge with classical NLP metrics to evaluate, compare, and monitor the quality of generated answers at scale.
Modern LLM-based systems (chatbots and RAG pipelines) are difficult to evaluate using traditional NLP metrics like BLEU and ROUGE, which often fail to capture semantic correctness and human-like judgment.
As a result, teams struggle to:
- Measure answer quality reliably
- Compare different models or prompts
- Detect performance degradation over time
EvalGuard solves this by introducing a structured evaluation framework:
- LLM-as-a-Judge with a fixed scoring rubric (1–5 scale)
- Classical NLP metrics (BLEU, ROUGE, BERTScore)
- Multi-model comparison (e.g., LLaMA vs Qwen)
- Benchmarking across datasets and prompts
This allows consistent, scalable evaluation aligned with human judgment.
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Input:
- Context / Article
- Question
- Generated Answer
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Evaluation:
- LLM-as-a-judge scoring (based on rubric: correctness, relevance, completeness, clarity)
- Traditional NLP metrics computation
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Output:
- Final evaluation score
- Metric comparison across models
- Dataset-level analysis
- Structured LLM-based evaluation rubric
- Automated scoring pipeline
- Model comparison (multiple LLMs)
- Batch evaluation support
- Data visualization and analysis
- Human-aligned evaluation validation (correlation analysis)
EvalGuard transforms subjective evaluation into a structured, repeatable, and scalable benchmarking system for LLM applications.
Compare answers generated by different models:
- LLaMA vs Qwen performance evaluation
- BLEU/ROUGE vs LLM-as-a-judge comparison
- Identification of superior model outputs based on human-aligned scoring
- Python
- Pandas
- OpenAI / GPT-4o (LLM-as-a-judge)
- NLP metrics (BLEU, ROUGE, BERTScore)
- Google Colab
- Bias mitigation in LLM evaluation
- Real-time evaluation API for production systems
- Dashboard for monitoring model performance over time
- Improved human alignment calibration
- ML Engineers working on RAG systems
- AI/LLM product teams
- Research labs evaluating generative models
- Companies deploying chatbots or QA systems
EvalGuard provides a reliable evaluation layer for LLM-based systems, enabling teams to measure, compare, and improve model performance with structured and scalable metrics.
Capstone Project – Data Science & LLM Evaluation System