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A practical implementation of Retrieval-Augmented Generation (RAG) to improve resume search and candidate evaluation by combining vector embeddings, keyword search, and reranking to accurately find relevant resume information and answer hiring-related queries. The system uses PostgreSQL with the pgvector extension to store and search embeddings efficiently, chunking resumes into overlapping segments for context, and employs a two-stage retrieval and reranking approach to balance speed and accuracy. Query expansion and hybrid search techniques improve recall and precision, while metrics like recall, precision, and latency are used to measure system performance. The approach also includes normalization of skill mentions and uses AI agents to reason over candidate-job fit.