Retrieval-augmented generation (RAG) empowers large language models (LLMs) with external knowledge but remains vulnerable to faithfulness hallucinations—answers that contradict retrieved evidence. We introduce RADAM, a lightweight post-processing framework that mitigates this issue via fine-grained hallucination detection and guided revision. RADAM leverages a single diagnostic critic, optimized via preference tuning on taxonomy-guided data, to generate structured critiques encompassing both precise error spans and explicit revision suggestions, which guide the base model toward more faithful outputs. Experiments on real-world datasets demonstrate that RADAM consistently achieves the lowest average hallucination rate across the evaluated methods, while largely preserving response quality and achieving out-of-domain performance comparable to enterprise-grade LLMs.