RAG Vs Fine Tuning: How To Choose The Right Method
In the realm of artificial intelligence, two prominent techniques have emerged to enhance the capabilities of large language models (LLMs): Retrieval-Augmented Generation (RAG) and LLM Fine-Tuning. These approaches serve different purposes, offering distinct advantages depending on the use case. In this blog, we’ll explore the key differences between RAG and LLM fine-tuning, how they work, and their respective benefits for businesses looking to deploy AI systems. What is Retrieval-Augmented Generation? Retrieval-Augmented Generation is an AI technique that combines traditional retrieval-based systems with generative models to create more accurate and contextually relevant content. Instead of relying solely on a pre-trained model’s knowledge, RAG actively retrieves external data to enhance its generative process. This real-time knowledge retrieval ensures that the responses are not only informed by pre-existing data but also reflect up-to-date information, making RAG ideal fo...