Summary
In this chapter, we explored the RAG approach, a powerful method for leveraging your data to craft personalized experiences, reduce hallucinations while also addressing the training limitations inherent in LLMs. Our journey began with an examination of foundational concepts such as vectors and databases, with a special focus on Vector Databases. We understood the critical role that Vector DBs play in the development of RAG-based applications, also highlighting how they can enhance LLM responses through effective chunking strategies. The discussion also covered practical insights on building engaging RAG experiences, evaluating them through prompt flow, and included a hands-on lab available on GitHub to apply what we’ve learned.
In the next chapter we will introduce another popular technique designed to minimize hallucinations and more easily steer the responses of LLMs. We will cover prompt engineering strategies, empowering you to fully harness the capabilities of...
