RAG vs. Fine-Tuning: Choosing the Right Approach for Your LLM Applications
In the rapidly evolving landscape of artificial intelligence, large language models (LLMs) have become powerful tools for a wide range of applications. However, these models come with inherent limitations that need to be addressed for optimal performance. Two methods stand out for enhancing LLM capabilities: Retrieval Augmented Generation (RAG) and Fine-Tuning. But which approach is right for your specific use case? Let’s break down the differences, strengths, and ideal applications for each. ...