Show HN: Python script to evaluate your RAG pipeline against a golden set (github.com)

A new Python script has been released to help developers evaluate Retrieval-Augmented Generation (RAG) pipelines against a "golden set," streamlining the identification of potential system failures before reaching end users. This tool offers five critical metrics, including Retrieval Recall@K, Answer Faithfulness, and Latency, providing developers insights on where their RAG systems may falter, such as not returning relevant document chunks or generating unfounded answers. Users can run the evaluation in seconds without needing API keys or extensive configuration. This initiative is significant for the AI/ML community as it addresses common pitfalls in RAG deployments, encouraging better evaluation practices and system reliability. The script equips developers with a straightforward way to benchmark their models, facilitating prompt adjustments based on specific performance scores. By focusing on both quantitative metrics and qualitative aspects like token overlap and cosine similarity between embeddings, it helps teams identify not just retrieval issues but also generation shortcomings, ultimately leading to a more robust implementation of RAG systems in real-world applications.