The tutorial discusses best practices for migrating prompts to open-source language models, emphasizing the advantages of using models like Mistral over proprietary ones such as GPT-3.5-turbo, particularly in terms of cost savings and privacy when running large numbers of inferences or dealing with sensitive data. The tutorial uses a specific prompt, "llm-investor," which retrieves data from a RAG pipeline to answer user queries, and compares its performance on Mistral and GPT models by using PromptLayer's batch evaluation tools. This involves setting up a dataset with sample questions and ground-truth answers, running the prompt on both models, and comparing their outputs and performance metrics. The tutorial highlights the importance of quick iteration in prompt engineering, facilitated by tools like PromptLayer, which allow for easy model switching and prompt template updates, leading to improved accuracy in evaluations. The approach promotes a development environment that supports rapid testing and refinement of prompts to achieve optimal results.