Summary
In this chapter, we deep-dived into the world of RAG models. We started by understanding the core principles of RAG and how they differ from traditional generative AI models. This foundational knowledge is crucial as it sets the stage for appreciating the enhanced capabilities that RAG brings to the table.
Next, we took a closer look at the architecture of RAG models, deconstructing their components through detailed code examples. By examining the encoder, retriever, and decoder, you gained insights into the inner workings of these models and how they integrate retrieved information to produce more contextually relevant and coherent outputs.
We then explored how RAG harnesses the power of information retrieval. These techniques help RAG effectively leverage external knowledge sources to improve the quality of a generated text. This is particularly useful for applications requiring high accuracy and context awareness. You also learned how to a simple RAG model using...
