Summary

Fine-tuning is a powerful technique for customizing models, but it may not always be necessary. As observed, it can be time-consuming and may have initial upfront costs. It’s advisable to start with easier and faster strategies, such as prompt engineering with few-shot examples, followed by data grounding using RAG. Only if the responses from the LLM remain suboptimal should you consider fine-tuning. We will discuss RAG and prompt engineering in the following chapters.

In this chapter, we delved into critical fine-tuning strategies tailored for specific tasks. Then, we explored an array of evaluation methods and benchmarks to assess your refined model. The RLHF process ensures your models align with human values, making them helpful, honest, and safe. In the upcoming chapter, we’ll tackle RAG methods paired with vector databases – an essential technique to ground your enterprise data and minimize hallucinations in LLM-driven applications.