Hi, I'm Kwang Yang
AI Engineer specializing in RAG
I'm an AI Engineer, passionate about building systems that solve real-world problems with the help of AI. My speciality is Retrieval-Augmented Generation (RAG), and I am also trained as a Data Scientist. I create efficient and scalable AI solutions.
Currently, I'm working on my Final Year Project, which utilises RAG to detect toxicity in games. I'm super interested in the Edtech and Gaming industries! In my free time, I make coffee, and I travel a lot. I also like to conduct workshops and give talks here and there.
Interested in some of my thoughts? Check it out here :)
Portfolio
Projects
Work Highlights
- SimpleAI - Redesigned Pinecone embedding system for faster and more accurate retrieval, and refactored it with existing tech stack of MongoDB and React
- Atlas - Designed RAG workflow with advanced methods such as Query Expansion and Rewriting, and cut client-side inference time by 69.84%
- NUS - Co-authoring a paper with Prof Linda Sellou for measuring interests in electrochemistry learning
Leadership & Teaching
- Media Director (AY24/25), NUS Raffles Hall
- Workshops Director (AY23/24), NUS Statistics and Data Science Society
- Student Lead (AY23/24), NUS Raffles Hall Developers
- Teaching Assistant (AY23/24 - AY24/25), IT1244 & other courses
Skills & Technologies
PythonSQLJavaScriptReactNumPypandasmatplotlibseabornscikit-learnSeleniumTensorflowPyTorchLangChainOpenAI APIPineconeHuggingFacePostgreSQLMySQLDockerAWSMongoDBFlaskNeo4jPythonSQLJavaScriptReactNumPypandasmatplotlibseabornscikit-learnSeleniumTensorflowPyTorchLangChainOpenAI APIPineconeHuggingFacePostgreSQLMySQLDockerAWSMongoDBFlaskNeo4j
Interested in collaborating or have a question?