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University admissions services must answer large volumes of applicant questions that differ substantially in complexity, ranging from repetitive FAQ-type requests to multi-step questions involving programs, entrance exams, admission rules, passing scores, and temporal comparisons. Ungrounded large language model responses are risky in this domain because answers must be factually correct, source-based, and consistent with official institutional data. This paper presents a three-tier hybrid architecture for an admissions dialogue assistant that combines deterministic FAQ matching, hybrid retrieval-augmented generation, and graph-grounded retrieval for complex queries. The first tier, Hash-FAQ, returns verified answers for frequent intents using normalized keys, hash-based lookup, near-duplicate fingerprinting, and semantic similarity checks. The second tier applies hybrid RAG based on BM25 retrieval, vector search, rank fusion, and optional cross-encoder reranking. The third tier uses GraphRAG to extract a constrained k-hop subgraph from a Neo4j knowledge graph built from relational admissions data and document-derived facts. All tiers are synchronized through a versioned indexing pipeline with shadow collections and atomic switching across lexical, vector, FAQ, relational, and graph stores. The system was evaluated using real admissions-campaign traffic and a labeled subset of applicant queries. Tier 1 resolved 68.7% of requests with low latency, while the GraphRAG branch improved factual accuracy with attribution on multi-step queries from 0.55 to 0.91 compared with the non-graph baseline. The main contribution of the study is a production-oriented, cost-aware retrieval-and-generation architecture that links tiered routing, synchronized knowledge publication, source attribution, and operational evaluation for applicant-facing institutional dialogue systems.
Stepanov et al. (Fri,) studied this question.