Introduction
Retrieval-Augmented Generation (RAG) has quickly become the most practical architecture for building enterprise AI applications that need to work with proprietary data. But there's a massive gap between a RAG demo that works on a laptop and a production system that handles millions of documents with sub-second latency.
At Kopfus, we've built RAG systems for clients across healthcare, fintech, and education. Here's our engineering playbook.
The Architecture
A production RAG pipeline has four critical layers:
1. Document Ingestion
The ingestion pipeline must handle diverse document formats (PDF, DOCX, HTML, Markdown) with intelligent chunking strategies. We use a combination of semantic and structural chunking to preserve context boundaries.
2. Embedding & Indexing
We typically use OpenAI's text-embedding-3-large or open-source alternatives like BGE-M3 for multilingual applications. The choice of vector database matters enormously at scale — we've had excellent results with Qdrant for its filtering capabilities and Pinecone for managed simplicity.
3. Retrieval Strategy
Simple cosine similarity isn't enough. Our production systems use a hybrid approach:
4. Generation & Guardrails
The final generation step needs careful prompt engineering, output validation, and citation tracking to ensure accuracy and trustworthiness.
Key Lessons Learned
Conclusion
Building production-ready RAG is an engineering discipline, not a prompt engineering exercise. It requires careful architecture, rigorous testing, and continuous monitoring. If you're building RAG for enterprise, reach out — we'd love to share more of what we've learned.