| Explainability remains a major challenge in AI-powered retrieval systems, particularly when large language models generate answers without transparent reasoning. We introduce a structured grounded reasoning framework that operationalizes explainability as a compositional property derived from explicit semantic provenance and rule-guided inference. Our approach integrates knowledge graphs, SPARQL-based retrieval, and dynamic prompt generation to ensure that responses are grounded in verifiable structured data and accompanied by interpretable reasoning traces. We formally define explainability criteria and implement the framework within a recommendation scenario. Experimental results show that our method significantly improves interpretability, traceability, and user trust compared to standard retrieval-augmented generation baselines while preserving retrieval accuracy. These findings demonstrate that explainability in LLM-driven systems can be systematically engineered rather than heuristically approximated. |
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