Exploration of Prompting Strategies for GraphRAG applications
KGC 2025
•
1h 46m
Brian O'Keefe, Amazon Web Services, Principal Neptune (Graph) Specialist Solutions Architect
In this hands-on learning tutorial, we will experiment with various strategies for using GenAI models to automate extracting and transforming raw data into a knowledge graph, and understanding and analyzing that knowledge graph. The outcome of this tutorial is to gain confidence and understanding how different techniques influence different outcomes and to be able to apply this knowledge to your GenAI and GraphRAG initiatives to improve results.
Up Next in KGC 2025
-
How to Model Reality: From Data to En...
Eliud Polanco, Fluree, President
Doug Beeson, Semantic Arts, Associate OntologistPharmaceutical and BioTechnology companies must comply with regulatory requirements, resulting in months of labor spent to produce textbooks of documentation that could be riddled with human errors or misinterpreta...
-
Building Agentic APIs With LLM Tool U...
William Lyon, Hypermode, Director of Developer Experience
The true power of LLMs isn’t in building chatbots, but rather leveraging AI models for implementing agentic workflows in the applications we build, adding features to our apps powered by LLMs that interact with APIs and data sources direc...
-
Unlocking Graph Neural Networks: A Ha...
Giuseppe Futia, CSI - Piedmontese Consortium for Information Systems (Italy), Data Engineer
Graphs provide a powerful framework for modeling relationships between entities, making Graph Neural Networks (GNNs) a crucial tool for applying machine learning to graph-structured data. However, impleme...