In the optimization journey, you can fine-tune Large Language Models (LLMs) to improve their performance for your use case.
Fine-tuning refers to the iterative process of making minor adjustments to a model's parameters or architecture to improve the model's performance on specific tasks.
See the video to learn more about improving your model's performance.
Compared to prompt engineering and RAG, fine-tuning is a resource-intensive and costly process. It requires substantial labeled data, computational resources, and expertise in deep learning models. It is important to know when to use fine-tuning, and when to avoid using it.
See the video to learn about the considerations of when to fine-tune LLMs, and when to avoid it.