RAG & Knowledge Systems

A RAG pipeline is either grounded in good retrieval or it hallucinates. There is no third option. The job is to combine the generative side of LLMs with precise retrieval from proprietary knowledge bases so the AI stays factually anchored in your own data. The work involves chunking strategies, embedding model selection and tuning, building and tuning vector databases, hybrid search with re-ranking, and the evaluation discipline that tells you when retrieval quality is actually improving. Advanced territory: multi-modal RAG, agentic RAG with iterative retrieval, knowledge graph integration. None of it works without the basics.