Retrieval Augmented Generation(RAG) with GPT-4

In this section, we will build an introductory program that implements RAG, Retrieval Augmented Generation. Document retrieval is not new. Knowledge bases have been around since the arrival of queries on databases decades ago. Generative AI isn’t new, either. RNNs were AI-driven text generators years ago. Taking these factors into account, we can say that RAG is not an innovation but an improvement that compensates for the lack of precision, training data, and responses of generative AI models. It can also avoid fine-tuning a model in some instances.There are also different ways of performing augmented generation, as we will see, among which:

• Chapter 11, Leveraging LLM Embeddings as an Alternative to Fine-Tuning, is where we will implement embedded data.
• Chapter 15, Guarding the Giants: Mitigating Risks in Large Language Models, in which one of the mitigating solutions is to implement knowledge bases.
• Chapter 20: Beyond Human-Designed...