10 Practical RAG Approaches: What Is Actually Useful and When to Use Each One

Hi everyone. With all the updates in the LLM stack over the past year, I decided to put together a practical list of RAG approaches that are actually useful in production or at least worth understanding if you are building LLM-based products. This is based on my own experience, research, and the patterns I keep seeing in real-world cases. What is RAG, in simple terms? RAG stands for Retrieval-Augmented Generation. It is an approach where the LLM does not answer only from its internal weights. Instead, it receives relevant context from an external knowledge base for a specific user query.