Fine-Tuning vs RAG

In this chapter, you'll dive into the world of enhancing pre-trained models and explore two key methods: Fine-Tuning and Retrieval-Augmented Generation (RAG). We’ll break down when to use each approach based on your specific needs. Whether you're dealing with rapidly changing data or need precise control over information retrieval, we’ll guide you through making an informed decision.

You'll discover the advantages of RAG for tasks where accuracy is paramount and data frequently updates. Plus, learn how Fine-Tuning can help when you want to add specialized knowledge or new features to your model. We provide a handy comparison table that outlines scenarios where each method shines, helping you choose the best fit for your project.

By the end of this chapter, you'll have a clear understanding of which approach suits your needs and why. You’ll also get tips on leveraging existing models and third-party services to streamline your development process. So, whether you're building an application from scratch or enhancing an existing one, this chapter will equip you with the knowledge to make smart choices about model customization.