Building a RAG (short for Retrieval Augmented Generation) to “chat together with your data” is straightforward: install a well-liked LLM orchestrator like LangChain or LlamaIndex, turn your data into vectors, index those in a vector database, and quickly arrange a pipeline with a default prompt.
Just a few lines of code and also you call it a day.
Or so that you’d think.
The fact 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 flawed, those quick-and-dirty demos are great for understanding the fundamentals. But in practice, getting a RAG system production-ready is about greater than just stringing together some code. It’s about navigating the realities of messy data, unexpected user queries, and the ever-present pressure to deliver tangible business value.
On 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 performance optimization — and discuss strategies to beat…
