Building a RAG (short for Retrieval Augmented Generation) to “chat with your data” is a fascinating journey in the realm of artificial intelligence. By utilizing a popular LLM orchestrator like LangChain or LlamaIndex, transforming data into vectors, indexing them in a vector database, and setting up a pipeline with a default prompt can be achieved effortlessly.
Just a few lines of code and voila, you have your RAG system up and running. Simple, right?
Well, not quite. The reality of implementing RAG goes beyond the simplicity of quick demos. Vanilla RAG setups are designed for brief showcases and may not effectively serve the demands of real-world business scenarios.
While quick demos help grasp the fundamentals, transitioning a RAG system into a production-ready state requires more than just coding skills. It involves addressing challenges such as disorganized data, unpredictable user queries, and the constant need to provide tangible business value.
In this post, we’re delving into the business imperatives critical for the success of a RAG-based project. Additionally, we’ll tackle technical obstacles ranging from data management to performance enhancement, and outline effective strategies to overcome these hurdles.