Hybrid Retrieval Architecture
Published in Red Hat, 2026
- Designed a hybrid retrieval pipeline combining sparse retrieval (BM25) and dense embedding-based search to improve document recall and ranking accuracy.
- Implemented vector search infrastructure using FAISS for efficient similarity search over large document collections.
- Integrated cross-encoder re-ranking models to improve final document ranking quality for downstream LLM tasks.
- Optimized chunking strategies and embedding generation for better semantic retrieval performance.
- Demonstrated improvements in retrieval accuracy and contextual relevance for knowledge-intensive LLM applications.