Designing an AI Recruitment System: Hybrid Retrieval and Explainable Ranking
TUKRUUU · reddit · 2026-08-08
A university recruitment system team is designing an AI-assisted module to generate ranked candidate shortlists with evidence based on natural-language queries.
The team prefers a hybrid architecture over feeding raw resumes directly to an LLM:
- Structured + Semantic: Parsing resumes into structured profiles for hard constraints (e.g., degree, experience) while using vector embeddings for semantic search (e.g., matching research specializations).
- Explainability: Recommends storing source text snippets for every extracted claim to show recruiters the 'why' behind recommendations.
The team is seeking community feedback on several technical points:
- Whether extracting structured data first outperforms raw RAG for semantic retrieval.
- Common pitfalls when combining deterministic filters with semantic relevance.
- Secure integration patterns with legacy ERPs lacking APIs without exposing production databases to AI.
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