Building Enterprise RAG Systems That Actually Work in Production

A RAG prototype is easy: point an embedding model at a folder of documents, wire up a vector store, and ask questions. That version works in a demo and falls apart within weeks of hitting real enterprise data, inconsistent document formats, stale content sitting alongside current policy, and retrieval that quietly returns the wrong chunk with high confidence.

The chunking strategy is usually the first thing that breaks. Fixed-size chunking ignores document structure entirely, splitting a table's header from its rows, or cutting a policy clause in half across a chunk boundary. We chunk along semantic and structural boundaries, sections, tables, and list items kept intact, with overlap tuned per document type rather than applied uniformly.

The second failure point is retrieval quality at scale. Cosine similarity over embeddings degrades once a knowledge base crosses tens of thousands of documents with overlapping terminology, several policies that all mention "termination," for example, each meaning something different by department. We layer metadata filtering and a reranking pass on top of vector search so the system narrows by document type and recency before it ever scores semantic similarity.

The third, and most consequential, is that most teams treat generation as the last step instead of a checked one. We put every generated answer through a grounding check that verifies each claim traces back to a retrieved passage, using the same claim-verification approach behind Hallucinate?™, before it reaches the user. An answer that can't be traced to a source doesn't get shown; it gets routed to a fallback or a human.

Deployed this way, RAG stops being a demo trick and becomes infrastructure: something a compliance team, a support desk, and an internal knowledge tool can all depend on without babysitting the outputs.