Some thoughts on Retrieval-Augmented Generation(RAG) and fine-tuning in LLMs: Both methods can improve the accuracy of LLMs but take different approaches. Imagine an LLM as a person who has read a lot, enabling it to provide correct answers when asked. However, its knowledge is limited to what it has read, so it might occasionally give incorrect answers (hallucinations). How can we make it answer more accurately?
 The first method is called RAG. This approach provides the LLM with additional information, combining its existing knowledge with external data to generate more accurate answers. The second method is fine-tuning, where the LLM is trained further on specific topics, turning it into an expert in that field and enabling it to provide more precise answers.
 So, which approach should you use? As the saying goes—it depends. If your questions focus on a specific field with specialized terminology, fine-tuning might be the better choice. On the other hand, if your question relies more on general knowledge, and the external data can be understood in plain language, RAG might be more suitable.
 Finally, it’s worth noting that these two approaches are not mutually exclusive—you can use both to achieve highly accurate results. However, the downside is that this combined approach can be both costly and time-consuming.