Empowering LLMs by hybrid retrieval-augmented generation for domain-centric Q&A in smart manufacturing

Checked 30 Sept 2026. Non-commercial or no-derivatives license: full text kept internally; only metadata and abstract are indexed and used.

Large language models (LLMs) have shown remarkable performances in generic question-answering (QA) but often suffer from domain gaps and outdated knowledge in smart manufacturing (SM). Retrieval-augmented generation (RAG) based on LLMs has emerged as a potential approach by incorporating an external knowledge base. However, conventional vector-based RAG delivers rapid responses but often returns contextually vague results, while knowledge graph (KG)-based methods offer structured relational reasoning at the expense of scalability and efficiency. To address these challenges, a hybrid KG-Vector RAG framework that systematically integrates structured KG metadata with unstructured vector retrieval is proposed. Firstly, a metadata-enriched KG was constructed from domain corpora by systematically extracting and indexing structured information to capture essential domain-specific relationships. Secondly, semantic alignment was achieved by injecting domain-specific constraints to refine and enhance the contextual relevance of the knowledge representations. Lastly, a layered hybrid retrieval strategy was employed that combined the explicit reasoning capabilities of the KG with the efficient search power of vector-based similarity methods, and the resulting outputs were integrated via prompt engineering to generate comprehensive, context-aware responses. Evaluated on design for additive manufacturing (DfAM) tasks, the proposed approach achieved 77.8% exact match accuracy and 76.5% context precision. This study establishes a new paradigm for industrial LLM systems, which demonstrates that hybrid symbolic-neural architectures can overcome the precision-scalability trade-off in mission-critical manufacturing applications. Experimental results indicated that integrating structured KG information with vector-based retrieval and prompt engineering can enhance retrieval accuracy, contextual relevance, and efficiency in LLM-based Q&A systems for SM.

• The significant average benefit of RAG quantified by Liu et al. 2025 is consistent with domain-specific evaluations: Wan et al. 2025 report 77.8% exact-match accuracy for hybrid RAG in manufacturing QA, and Gaber et al. 2025 benchmarked an LLM workflow incorporating RAG on 2,000 MIMIC-derived medical cases for triage, referral, and diagnosis support. Trace →
• Hybrid retrieval that combines vector or semantic search with keyword matching or knowledge-graph structure appears independently in fire investigation (Choi & Cho 2026), smart manufacturing (Wan et al. 2025), and biomedicine (Matsumoto et al. 2024), suggesting a convergent design pattern for making domain RAG systems reliable. Trace →

• RAG reliability and evaluation