RAG Feature Pipeline
Retrieval-augmented generation (RAG) is fundamental in most generative AI applications. RAG’s core responsibility is to inject custom data into the large language model (LLM) to perform a given action (e.g., summarize, reformulate, and extract the injected data). You often want to use the LLM on data it wasn’t trained on (e.g., private or new data). As fine-tuning an LLM is a highly costly operation, RAG is a compelling strategy that bypasses the need for constant fine-tuning to access that new data.
We will start this chapter with a theoretical part that focuses on the fundamentals of RAG and how it works. We will then walk you through all the components of a naïve RAG system: chunking, embedding, and vector DBs. Ultimately, we will present various optimizations used for an advanced RAG system. Then, we will continue exploring LLM Twin’s RAG feature pipeline architecture. At this step, we will apply all the theoretical aspects we...
