RAG & LLM Frameworks: Learn about practical graph design patterns and retrieval strategies to more effectively customize GenAI for real-world applications. While GenAI offers great potential, it faces challenges with hallucination and lack of domain knowledge. Graph-powered retrieval augmented generation (GraphRAG) helps overcome these challenges by integrating vector search with knowledge graphs and data science techniques to improve context, semantic understanding, and personalization while facilitating real-time updates. You'll receive detailed coded examples to begin your journey with GenAI and graphs, leaving with practical skills to apply immediately to your own projects. Prerequisites: This is a hands-on workshop where you can follow along with Jupyter notebooks in Colab. We will provide links to notebooks at the beginning of the workshop. To follow along please: - Bring your laptop - Create a Google account ahead of time if you don’t have one already so you can run Colab notebooks. - [Prefered] Bring a working openAI API key. We will provide a key for those that do not have one yet. To make a new key, create an OpenAI account or sign in. Next, navigate to the API key page and "Create new secret key". Optionally naming the key. Save this somewhere safe, and do not share it with others. We recommend testing your key to make sure it works - see the OpenAI quickstart tutorial for more details.