Retrieval Augmented Generation (RAG) is a great way to harness the power of generative AI for information not contained in a LLM’s training data and to avoid depending on LLM for factual information. However, RAG only works when you can quickly identify and supply the most relevant context to your LLM. This book shows you how to use knowledge graphs to model your RAG data and deliver better performance, accuracy, traceability, and completeness.

Plus, the same offer also applies to LangChain in Action and Hugging Face in Action.