A Scalable AI Recruitment Framework Using RAG and Explainable Multi-Agent Decision-Making

Abstract: Recruitment processes increasingly rely on Artificial Intelligence (AI) to automate resume screening, candidate-job matching, skill-gap identification, and candidate evaluation. However, conventional AI and Large Language Model (LLM)-based recruitment systems may generate responses that are insufficiently grounded in the actual information contained in resumes and job descriptions, limiting their transparency, reliability, and explainability. This research presents a scalable Retrieval-Augmented Generation (RAG) framework for explainable AI-based recruitment that integrates semantic retrieval with LLM-based response generation. Candidate resumes and job descriptions are processed into overlapping text chunks and converted into semantic embeddings using all-MiniLM-L6-v2, which are indexed in ChromaDB for efficient similarity-based retrieval. For a given recruitment query, the system retrieves the most relevant evidence using different Top-K configurations (K ∈ {1, 3, 5, 10}) and provides the retrieved context to Qwen3-14B Instruct for evidence-grounded response generation. The framework supports resume-based question answering, job-requirement analysis, candidate-job matching, skill-gap identification, and evidence-based explanations. A recruitment benchmark of 500 queries, distributed across five categories (Resume Information, Job Requirements, Candidate-Job Matching, Skill-Gap Identification, and Explainability) is used to evaluate the framework. Retrieval performance is assessed using Precision@K, Recall@K, Mean Reciprocal Rank (MRR), and NDCG, while generated responses are evaluated using Context Precision, Context Recall, and Answer Relevance. The experimental analysis demonstrates that retrieval depth substantially influences response quality, with lower Top-K configurations providing stronger contextual precision and more consistent answer relevance. The proposed framework therefore provides a systematic approach for integrating retrieval, generation, and evidence tracing to support more context-aware, transparent, and explainable AI-assisted recruitment.

Keywords: Retrieval-Augmented Generation, Explainable AI, AI-Based Recruitment, Large Language Models, Semantic Retrieval.