Summary
RAG is one of the fastest-growing paradigms in the field of LLMs. Eliminating hallucinations is one of the most important challenges and one of the most problematic constraints for LLMs and agents to be put into production. RAG is also a flexible system that has several advantages over fine-tuning. As we have seen, this system can be updated frequently with minimal cost and is compatible with different types of data. The naïve RAG is the basic system, consisting of three main components: an LLM, an embedder, and a vector database.
In the next chapter, we will see how this system is evolving. There are now many new additional components, which we will also look at. Despite RAG, sometimes the model still hallucinates as if it ignores the context. This is why sophisticated components have evolved, which we will look at in detail. We will also discuss the subtle interplay between parametric memory and context.
