Architecting Context-Aware RAG Systems for Enterprise Data

Why Naive RAG Fails in Production

Most basic RAG tutorials split documents every 500 characters and query a vector database. In enterprise documents with complex tables, financial disclosures, and multi-paragraph legal terms, this naive chunking splits critical context across chunks.

We implement semantic markdown parsers that respect section boundaries and extract tabular data into structured JSON, ensuring the LLM receives the complete contextual picture when formulating responses.