After implementing RAG (Retrieval-Augmented Generation) systems across multiple enterprise clients, I’ve learned that the gap between a demo and a production-ready system is wider than most tutorials suggest. Here’s what actually matters.

The Promise vs. Reality

RAG sounds simple: embed your documents, store them in a vector database, retrieve relevant chunks, and feed them to an LLM. The demos are impressive. Then you try it with real enterprise data.

Your PDFs have tables that don’t parse correctly. Your SharePoint documents have inconsistent formatting. Your legacy systems export data in formats that make you question humanity’s choices. Welcome to enterprise RAG.

Architecture Decisions That Matter

Chunking Strategy

The default “split by 500 tokens” approach works for blog posts. For enterprise documents, you need smarter chunking:

  • Semantic chunking: Split by meaning, not arbitrary token counts
  • Hierarchical chunking: Maintain parent-child relationships for context
  • Metadata preservation: Keep document titles, section headers, and dates attached

I’ve found that spending 60% of your time on chunking strategy yields better results than any amount of prompt engineering later.

Vector Database Selection

For enterprise, consider:

  • Pinecone: Great managed option, scales well
  • Weaviate: Open-source, excellent hybrid search
  • pgvector: If you’re already on PostgreSQL, often good enough
  • Qdrant: Fast, good filtering capabilities

The “best” choice depends on your existing infrastructure, not benchmark scores.

Retrieval Quality

Hybrid search (combining vector similarity with keyword matching) consistently outperforms pure vector search for enterprise use cases. Your users will search for “Q4 2025 revenue report” and expect exact matches, not semantic approximations.

Common Pitfalls

  1. Ignoring access control: Enterprise data has permissions. Your RAG system needs to respect them.

  2. Over-relying on embeddings: Some queries need structured search. “Show me all contracts expiring in March” isn’t a vector search problem.

  3. Neglecting evaluation: Without systematic evaluation, you’re just vibes-testing. Build evaluation datasets early.

  4. Underestimating latency: Enterprise users expect sub-second responses. Plan your architecture accordingly.

What Actually Works

Start small. Pick one document type, one use case, one user group. Get that working well before expanding. The companies that succeed with RAG are the ones that resist the temptation to boil the ocean.

The technology is mature enough for production. The challenge is understanding your data and your users well enough to build something they’ll actually use.

Building a RAG system for your organization? Let’s talk about architecture and implementation strategies.