Large Language Models alone frequently hallucinate or produce generic responses when querying proprietary or rapidly evolving company data. Retrieval-Augmented Generation (RAG) bridges this gap by grounding model outputs with precision context retrieved dynamically at runtime.
Building a production-ready RAG pipeline requires far more than basic vector cosine similarity. High accuracy necessitates hybrid retrieval, dynamic chunking, and intelligent cross-encoder re-ranking.
Document Parsing, Chunking and Vector Embeddings
High quality retrieval begins with meticulous document parsing. Splitting markdown, PDF tables, and code files into semantic chunks prevents losing critical contextual continuity.