Building an Enterprise RAG System for Non-English Documents: A Turkish Case Study

Building an Enterprise RAG System for Non-English Documents: A Turkish Case Study Retrieval-Augmented Generation (RAG) systems are revolutionizing how we interact with information. They allow us to build powerful question-answering applications that can leverage internal knowledge bases, improving accuracy and reducing hallucination compared to relying solely on large language models (LLMs). While many examples focus on English documents, the real challenge lies in adapting RAG to other languages, especially those with complex morphologies like Turkish. In this article, we'll dive into the practical aspects of building a production-ready RAG system for Turkish documents. We'll explore specific challenges, implementation details, and benchmark results achieving a 93% recall rate. This guide will be useful for anyone looking to implement RAG for languages beyond English or simply seeking a more robust RAG architecture. The Challenge: Beyond Simple Text Splitting RAG systems typically inv