Silverchair and OpenSource Connections co-designed an evaluation pipeline for benchmarking Retrieval-Augmented Generation (RAG) architectures on real publisher content. Built with Python, LlamaIndex, RAGAS and LangSmith, it generates question-context-answer triplets, runs RAG models, and scores outputs on faithfulness, answer relevance, context precision/recall and answer correctness.
Benefits & outcomes
Metrics rather than vibes for judging RAG performance.
Different architectures and models can be benchmarked quickly, with automated hyperparameter tuning.
Publishers get a test bed for weighing strategy, cost and latency trade-offs.
Less hallucination, better-grounded answers.
Publisher / company: Silverchair (in partnership with OpenSource Connections)
Date of mention: 01/01/2025
Source: https://opensourceconnections.com/case-studies/silverchair-rag-evaluation/