A collaborative system to benchmark and compare Retrieval-Augmented Generation (RAG) architectures using real-world publisher content. Silverchair and OpenSource Connections co-designed a robust evaluation pipeline—using Python, LlamaIndex, RAGAS, and LangSmith—to generate question-context-answer triplets, run RAG models, and score outputs across metrics such as faithfulness, answer relevance, context precision/recall, and answer correctness.
Benefits & outcomes
- Metrics-driven insights into RAG performance
- Ability to benchmark different architectures/models quickly
- Framework supports automated hyperparameter tuning and rapid iteration
- Publishers gain a “dynamic playground” to test strategies, costs, and latency trade-offs
- Reduced hallucination and improved answer grounding
Publisher / company: Silverchair (in partnership with OpenSource Connections)
Date of case study: 01/01/2025
Source: https://opensourceconnections.com/case-studies/silverchair-rag-evaluation/