Comparing Retrieval-Augmented Generation With Fine-Tuning

Large language models (LLMs) like GPT-4 or Claude Opus are highly versatile but often lack the ability to provide up-to-date information due to the infrequency of their training updates. Retrieval-augmented generation (RAG) and fine-tuning are two techniques that address this limitation by allowing models to access new information. RAG works by incorporating additional data sources into prompts, which enables the model to reference current and specific data that it wasn't originally trained on, thus improving accuracy and source citation. It involves a setup stage for data collection and embedding, followed by a retrieval stage where prompts are enriched with relevant data chunks. In contrast, fine-tuning involves training the model with additional data to improve its performance in specific areas or styles without the need for large prompts, making it suitable for tasks requiring consistent output formats or specialized knowledge. While RAG is ideal for accessing constantly changing data and ensuring factual correctness, fine-tuning is beneficial for optimizing prompt sizes, modifying output styles, focusing on specific fields, and enhancing small models to become specialists in particular domains.