Prompt engineering techniques have been viewed as the future of interactions with Large Language Models (LLMs), but they also have limitations that can lead to hallucinations and require significant time for individual prompts. LLM fine-tuning is another approach that leverages an existing knowledge base of an LLM combined with task-specific training to optimize models' performance, proving particularly useful for high-volume cases. Retrieval Augmented Generation (RAG) allows LLMs to access external knowledge sources to complete tasks, making it suitable for knowledge-intensive tasks or data that may change over time. Each approach has its benefits and challenges, and the choice of method depends on the presence of factors such as resources and task specifics.