Afif A. Iskandar
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.
Session
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.