Rationale-Augmented Retrieval with Constrained LLM Re-Ranking for Task Discovery

• URL: http://arxiv.org/abs/2510.05131v1
• Date: Wed, 01 Oct 2025 01:28:59 GMT
• Title: Rationale-Augmented Retrieval with Constrained LLM Re-Ranking for Task Discovery
• Authors: Bowen Wei,
• Abstract summary: Head Start programs utilizing GoEngage face significant challenges when new or rotating staff attempt to locate appropriate Tasks on the platform homepage.<n>These difficulties arise from domain-specific jargon, system-specific nomenclature, and the inherent limitations of lexical search in handling typos and varied word ordering.<n>We propose a pragmatic hybrid semantic search system that combines lightweight typo-tolerant lexical retrieval, embedding-based vector similarity, and constrained large language model (LLM) re-ranking.
• Score: 4.061135251278187
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Head Start programs utilizing GoEngage face significant challenges when new or rotating staff attempt to locate appropriate Tasks (modules) on the platform homepage. These difficulties arise from domain-specific jargon (e.g., IFPA, DRDP), system-specific nomenclature (e.g., Application Pool), and the inherent limitations of lexical search in handling typos and varied word ordering. We propose a pragmatic hybrid semantic search system that synergistically combines lightweight typo-tolerant lexical retrieval, embedding-based vector similarity, and constrained large language model (LLM) re-ranking. Our approach leverages the organization's existing Task Repository and Knowledge Base infrastructure while ensuring trustworthiness through low false-positive rates, evolvability to accommodate terminological changes, and economic efficiency via intelligent caching, shortlist generation, and graceful degradation mechanisms. We provide a comprehensive framework detailing required resources, a phased implementation strategy with concrete milestones, an offline evaluation protocol utilizing curated test cases (Hit@K, Precision@K, Recall@K, MRR), and an online measurement methodology incorporating query success metrics, zero-result rates, and dwell-time proxies.

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