Large RAG systems need more than retrieval accuracy checks

A large legal document database needs a separate way to judge the quality of its retrieval step before it can support reliable retrieval-augmented generation. If the documents have a steady structure, such as sections, subsections, and paragraphs, hierarchical chunking can split them into natural context units. The search setup may use hybrid search, combining vector search with keyword search, and the weights between those methods may need tuning.

Precision can be measured by creating questions, checking the top returned chunks, and grading whether they are useful. Recall is much harder because it would require knowing every relevant chunk in the whole corpus, which is not practical for thousands of dense legal documents. Related discussion points out that production RAG often fails after retrieval, not only during retrieval.

Retrieved chunks may need re-ranking, careful context selection, and a separate check that the model does not combine correct chunks into a wrong final answer. A solid evaluation setup should therefore include human-checked examples, failure cases, and tests for both retrieval quality and answer synthesis.