The article examines the comparative properties of Retrieval-Augmented Generation algorithms and large-language-model fine-tuning methods in the context of domain-specific search tasks with a high cost of error. The aim is to identify operating regimes in which RAG and fine-tuning differentially affect the accuracy of top-ranked results, the evidential quality of answers, and the safety of handling sensitive data. The relevance of the study is driven by the rapid growth of industrial domain-specific search systems that must simultaneously ensure knowledge updatability, strict citation-based verifiability, and regulatory discipline. The novelty lies in the fact that the comparison is conducted not in the abstract form of RAG versus fine-tuning, but at the level of individual pipeline components and from the perspective of operational trade-offs: it is shown that retrieval and ranking form a truth scaffold and a channel for knowledge refresh, whereas fine-tuning acts as a delicate regulator of format, terminology, and epistemic precision without resolving the problem of obsolescence in parametric representations. The article concludes in favor of hybrid schemes that combine hybrid retrieval, reranking, and strict citation rules with lightweight, parameter-efficient model adaptations, thereby enabling reproducible, controllable, and scalable operation of domain-specific search systems. The article is intended for researchers in information retrieval, engineers of applied RAG systems, and practitioners deploying generative models in high-risk domains.

domain-specific search, Retrieval-Augmented Generation, LLM fine-tuning, hybrid retrieval, reranking

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