With over 20 years of experience working with large customers across multiple sectors, I have held roles including Support Consultant, Technical Account Manager, and Customer Success Architect. I spent a significant part of my career working with IBM, where I developed deep expertise in Java applications, specializing in performance tuning and optimization, along with extensive experience in relational databases.
In recent years, I transitioned to working with Neo4j graph databases, which led to my involvement in cutting-edge technologies like generative AI and GraphRAG. I apply my expertise in Python, Java, and Cypher to help organizations unlock the value of their data through innovative solutions.
Currently based in London, I work with EMEA customers to help them make sense of their data, providing insights and support that drive business value.
In this GenAI workshop, you will learn how Knowledge Graphs and Retrieval Augmented Generation (RAG) can support your GenAI projects.
GenAI and Large Language Models (LLMs) have the potential to increase productivity and provide access to data, but they need grounding and good context to be truly useful:
In this workshop, you will:
- Use Vector indexes and embeddings in Neo4j to perform similarity and keyword search
- Use Python, LangChain and OpenAI to create a Knowledge Graph of unstructured data
- Learn about Large Language Models (LLMs), hallucination and integrating knowledge graphs
- Explore Retrieval Augmented Generation (RAG) and its role in grounding LLM-generated content
After completing this workshop, you will be able to explain the terms LLM, RAG, grounding, and knowledge graphs. You will also have the knowledge and skills to create simple LLM-based applications using Neo4j and Python.
This workshop will put you on the path to controlling LLMs and enabling their integration into your projects.
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