What Nobody Tells You About RAGs | Towards Data Science
B uilding a RAG (short for Retrieval Augmented Generation) to “chat with your data” is easy: install a popular LLM orchestrator like LangChain or LlamaIndex, turn your data into vectors, index those in a vector database, and quickly set up a pipeline with a default prompt. A few lines of code and you call it a day. Or so you’d think. The reality is more complex than that. Vanilla RAG implementations, purposely made for 5-minute demos, don’t work well for real business scenarios. Don’t get me wrong, those quick-and-dirty demos are great for understanding the basics. But in practice, getting a RAG system production-ready is about more than just stringing together some code. It’s about navigating the realities of messy data, unforeseen user queries, and the ever-present pressure to deliver tangible business value. In this post, we’ll first explore the business imperatives that make or break a RAG-based project. Then, we’ll dive into the common technical hurdles — from data handling to per
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