RAG-TESTER: Automated End-to-End Testing of Retrieval-Augmented Large Language Models

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arXiv:2608.00054v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) enables Large Language Models (LLMs) to use external and domain-specific knowledge, but its reliability depends on the interaction between the generative model, embedding model, retrieval mechanism, and prompt construction strategy. We present RagTester, an automated end-to-end testing approach for RAG systems. RagTester generates retrieval documents, test inputs, and expected outputs; executes the tests; and e