Think Different

  RAG vs. Fine-Tuning: A Comparative Analysis RAG (Retrieval-Augmented Generation) and Fine-Tuning are two primary techniques used to enhance the capabilities of large language models (LLMs). While they share the goal of improving model performance, they achieve it through different mechanisms.   RAG (Retrieval-Augmented Generation) How it works: RAG involves retrieving relevant information from a vast knowledge base and incorporating it into the LLM's response generation process. The LLM first searches for pertinent information based on the given prompt, then combines this retrieved context with its pre-trained knowledge to generate a more informative and accurate response.   Key characteristics: Dynamic knowledge access: RAG allows the LLM to access and utilize up-to-date information, making it suitable for tasks that require real-time data.   Improved accuracy: By incorporating relevant context, RAG can reduce the likelihood of hallucinations or gener...