Building 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 performance optimization — and discuss strategies to overcome…
