Newfront's RAG Retrieval Evaluation Framework

About this template

A methodology for systematically improving AI search and chat products by debugging the intermediate retrieval steps rather than just the final output. This framework outlines how to create ground truth datasets, automate grading via fuzzy string matching, and run experiments (such as Reranking) to balance retrieval recall against precision.

"Shift the focus of AI quality assurance from subjective 'Answer Evaluation' to objective 'Retrieval Evaluation.' By isolating and testing the retrieval component separately from the generation component using ground truth pairs (query + source excerpt), teams can identify 'garbage in' failures 20x faster than testing final answers and scientifically determine the optimal context cutoff threshold."