RAG in Enterprise: Lessons from Real Deployments
By Eyup Turkay, CEO
Retrieval-Augmented Generation has become one of the most discussed patterns in enterprise AI. After deploying RAG systems for multiple clients across healthcare, logistics, and financial services, our team has developed a clear picture of what works and what does not.
What We Found
The biggest misconception is that RAG is a plug-and-play solution. In reality, the quality of your retrieval layer determines 80% of the output quality. We invested heavily in chunking strategies, metadata enrichment, and hybrid search (combining vector similarity with keyword matching) before seeing consistently useful results.
Document preprocessing is where most projects succeed or fail. Our team built custom pipelines for each client that handled OCR, table extraction, and hierarchical document structure — not just naive text splitting. The difference in answer quality was dramatic.
Our Recommendation
Start with a narrow, well-defined knowledge base rather than trying to index everything. Measure retrieval precision before worrying about generation quality. And always build a human feedback loop into the system from day one — it is the fastest path to production-grade accuracy.