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This course will teach you to enhance LLM performance using knowledge graph retrieval-augmented generation and Neo4j.

Generative AI with LLMs and Knowledge Graphs using Neo4j.png

neo4jllmkgstreamlit.tar.gz

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This course will guide you in enhancing LLMs’ performance, response accuracy, and cohesiveness using knowledge graphs. You’ll learn to construct and refine knowledge graphs from raw text and use Neo4j for effective graph storage and querying. You’ll also learn to integrate knowledge graphs with LLMs, improving their performance. Ideal for data scientists, developers, and AI enthusiasts, this course provides the tools to advance your skills in modern AI technologies.

Moreover, you’ll be introduced to utilizing knowledge graphs in retrieval-augmented generation (RAG), followed by exploring traditional and advanced named entity recognition (NER) and relationship extraction techniques. The course will then introduce Neo4j, teaching you to store LLM-extracted entities effectively. Finally, you’ll learn how to enhance LLM performance through knowledge graph-based RAG.

This course will equip learners to effectively leverage knowledge graphs in real-world applications, enhancing their expertise in generative AI.

Master Knowledge Graph Retrieval-Augmented Generation with Neo4j

Review what we have learned in this course.

Conclusion

Introduction to the Course

Constructing Knowledge Graphs

Neo4j: Storing Entities and Relationships to Graph Database

Integrating Knowledge Graphs with LLMs

Conclusion