GRADRAG

GRADRAG: Cross-component prompt adaptation for coordinated multi-agent RAG pipelines

GRADRAG is a new framework that treats a multi-agent RAG pipeline as a computational graph and propagates structured evaluator feedback upstream to iteratively update retriever, graph constructor, and answerer prompts. Unlike prior work that optimizes components in isolation, GRADRAG coordinates improvements across the full pipeline via a Prompt Optimizer and an early-stopping Evaluator. Evaluated on SQUALITY and QMSUM benchmarks under both flat chunk-based and graph-based retrieval paradigms, GRADRAG achieves a 12–15 percentage point net preference margin over one-step refinement baselines in LLM-judged pairwise comparisons.