Optimizing Domain-Specific RAG Architectures for Low-Resource Languages: An Ablation Study for Institutional Knowledge Assistants in Turkish Higher Education

In higher education, hallucination-free information retrieval is critical for navigating complex institutional legislation. This study develops a highly accurate, production-ready Retrieval-Augmented Generation (RAG) architecture tailored to Turkish, a morphologically rich, low-resource language. We evaluate the pipeline using an expert-curated dataset of 846 canonical question–answer pairs derived from real-world student inquiries. Rigorous statistical testing reveals that while top embedding models achieve comparable, statistically indistinguishable retrieval accuracy, BAAI/bge-m3 demonstrates robust representational symmetry by maintaining identical document rankings across normalized distance metrics. Challenging common literature assumptions, our architectural ablation shows that dense retrieval alone is highly sufficient; hybrid (dense + sparse) fusion yields no meaningful accuracy gain when indices are constructed over equivalent text. At the generation layer, an efficiently sized 9-billion-parameter model matches or surpasses the response quality of much larger models—a ranking confirmed via repeated-run significance testing—while operating 3.9 times faster than a 32-billion-parameter counterpart and 2.5 times faster than an 8-billion-parameter alternative. Finally, we demonstrate that the latency benefits of INT8 Scalar Quantization are highly scale-dependent, accelerating retrieval in small-scale micro-benchmarks but increasing end-to-end latency at larger index scales. Ultimately, this study establishes that optimizing a production RAG system relies on selecting highly aligned, efficient components rather than maximizing architectural complexity or parameter count.