The growing deployment of large language models (LLMs) in policy-sensitive and distributed environments raises concerns about hallucinations, semantic opacity, and lack of traceability, especially in regulated domains such as healthcare and finance. Retrieval-augmented generation (RAG) helps address these issues by grounding outputs in external sources, while data spaces (DSs) offer a governance-driven infrastructure for federated and sovereign data exchange. However, their integration remains underdeveloped, limiting the design of AI systems that are both explainable and compliant with regulatory demands. This paper analyzes three RAG–DSs integration models from the literature and introduces two new architectures, Guided RAG and Federated RAG, that directly address their structural limitations by combining coordination, data sovereignty, and trust in generative systems. Guided RAG and Federated RAG are formally specified using Business Process Model and Notation (BPMN), and all five models, together with a non-retrieval baseline, are analyzed across operational dimensions including transparency, traceability, and control delegation. This work establishes an architectural foundation for trustworthy generative AI operating under distributed governance constraints, with explicit guarantees of data sovereignty. The proposed framework supports systematic evaluation in federated settings and serves as a foundation for methodological refinement and regulatory alignment.