Case Study
RAG Evaluation System
A benchmark framework for comparing LLMs on scripture-based question answering with retrieval and quantitative evaluation.
Architecture
Queries are embedded, matched against a FAISS index, and passed to the LLM with retrieved context.
Evaluation pipelines compare baseline and retrieval-augmented outputs across several models.
Metrics are collected automatically so model behavior can be compared consistently.
Screenshots
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Retrieval
Embedding, retrieval, and model comparison workflow.
Challenges
- Measuring quality across models that produce different response styles.
- Keeping retrieval relevant without overfitting to the evaluation dataset.
- Making benchmark output easy to compare across runs.
Benchmarks
Automated benchmarking
Grounded retrieval
Quantitative reports
Lessons Learned
- Evaluation quality improves when retrieval and scoring are treated as first-class pipeline stages.
- Comparability is as important as raw score output in research tooling.
Tech Stack
PythonFAISSSentence TransformersHugging FaceLLM APIs
Key Results
Automated benchmarking
Grounded retrieval
Quantitative reports