Master Knowledge Graph Retrieval-Augmented Generation with Neo4j

Knowledge graphs are powerful tools that structure information into entities and relationships, making data more accessible and meaningful for AI applications. They are essential for enhancing the performance of LLMs by providing structured context, improving response accuracy and cohesiveness, and reducing hallucinations on datasets outside the LLM’s training data. In this course, you’ll explore knowledge graphs for retrieval-augmented generation (RAG) and dive deep into traditional to advanced NER and relationship extraction techniques. You’ll learn to construct and refine knowledge graphs from raw text, store and query them effectively with Neo4j, and integrate them with LLMs to boost their performance and build personalized chatbots using custom datasets. After completing this course, you’ll gain expertise in implementing graph RAG for complex scenarios, advancing your skills in building generative AI applications.