2024-10-27 –, CLASS #4
Language: English
This talk offers a thorough and balanced review of using Graph Databases (GraphDB) to enhance the knowledge bases of Large Language Models (LLMs). Drawing from practical experiences and real-world applications, we will present both the advantages and challenges of integrating GraphDB with LLMs.
We will start by exploring the capabilities and limitations of generative AI and LLMs, emphasizing common issues such as hallucination, where models generate misleading or baseless content. The core of the presentation will delve into how GraphDB can provide a structured and reliable knowledge base that improves the contextual accuracy of LLM outputs.
Attendees will gain insights into the practical implementation of GraphDB, supported by hands-on examples and case studies. We will discuss the strengths of GraphDB, such as its ability to create a robust and interconnected knowledge graph, and also address the potential drawbacks and challenges encountered during implementation.
By the end of the session, participants will have a clear understanding of the real-world impact of using GraphDB with LLMs, equipping them with the knowledge to make informed decisions about their AI projects. This talk is designed to be both informative and practical, offering deep insights into the intersection of GraphDB and LLM technologies.
In the rapidly evolving landscape of AI and machine learning, Large Language Models (LLMs) have shown remarkable capabilities in generating human-like text. However, one of the significant challenges faced by LLMs is the issue of hallucination, where the models generate misleading or baseless content. This talk seeks to explore whether Graph Databases (GraphDB) can be the solution to this problem, enhancing the accuracy and reliability of LLM outputs.
We will start by discussing the foundational concepts of generative AI and LLMs, setting the stage for understanding the inherent limitations of these models. The presentation will then pivot to the integration of GraphDB, detailing how this technology can provide a structured and interconnected knowledge base that grounds the outputs of LLMs in reliable data.
Our session will provide a balanced view, showcasing both the benefits and challenges of using GraphDB with LLMs. Through real-world examples and hands-on case studies, attendees will learn about the practical aspects of this integration, including the enhanced data organization and retrieval capabilities offered by GraphDB. We will also candidly discuss the potential difficulties and limitations encountered during the implementation process, providing a comprehensive understanding of what it takes to successfully leverage GraphDB in AI projects.
By the conclusion of this talk, participants will be well-equipped with the knowledge to assess the real-world impact of GraphDB on LLMs. They will be prepared to make informed decisions about their AI projects, weighing both the potential advantages and the obstacles. This presentation aims to inspire and educate, providing deep, practical insights into whether GraphDB can truly transform the capabilities of LLMs and act as a game changer in the field of AI.
Afif Akbar Iskandar, a data science professional with over 9 years of experience in the field.
Having earned a Bachelor's degree in Mathematics and a Master's degree in Computer Science from Universitas Indonesia, Afif boasts a solid academic foundation in the field.
As a dedicated data science mentor, Afif utilizes his extensive knowledge to educate others. Driven by his enthusiasm for technology, he operates the YouTube channel "NgodingPython," featuring insightful content on Python programming, data science, IoT, and beyond.