"RAG (Retrieval-Augmented Generation) is solving the hallucination problem faster than fine-tuning ever could. For 90% of enterprise use cases, connecting an LLM to your own vector database is the winning strategy."
The Deep Dive
While fine-tuning embeds knowledge into the model's weights (static and expensive), RAG injects knowledge into the context window (dynamic and cheap). This distinction is critical for businesses with rapidly changing data. We are seeing a complete shift towards RAG architectures for internal knowledge bases and customer support bots.