Intelligent RAG Systems
Retrieval-augmented generation, vector search, knowledge indexing.
I build retrieval systems that give language models access to your actual data — documents, codebases, knowledge bases — so answers are grounded in facts, not hallucination.
The architecture covers the full pipeline: document ingestion, chunking strategy, embedding model selection, vector store configuration, and retrieval-generation orchestration.
The difference between a RAG system that works and one that doesn't is almost always in the details — how you chunk, what you index, and how you rank results.
Retrieval layers
Hybrid search combining vector similarity and keyword matching
Index strategy
Chunking, metadata enrichment, and hierarchical indexing
Grounded answers
Citation-backed responses with source attribution
Knowledge shaping
Curating and structuring data for optimal retrieval