23 June 2025
13 min read
What is RAG and Why Does It Matter?
Retrieval-Augmented Generation solves the fundamental problem of LLMs: they only know what they were trained on. RAG gives an LLM access to your specific knowledge — company documentation, product manuals, research papers, internal databases — at query time, without retraining the model.
The alternative, fine-tuning, requires thousands of labeled examples, GPU time, and re-training whenever your knowledge changes. RAG requires none of that. When a user asks a question, you retrieve the relevant documents from your knowledge base, inject them into the prompt, and let the LLM generate an answer grounded in your actual data. The result is dramatically more accurate and up-to-date than a standalone LLM.
