Cheney Zhang

Algorithm Engineer

Cheney Zhang is an accomplished Algorithm Engineer at Zilliz. With a profound passion for and expertise in cutting-edge AI technologies such as LLMs and Retrieval Augmented Generation (RAG), Cheney has actively contributed to many innovative AI projects, including Towhee, Akcio, and OSSChat. Before joining Zilliz, he worked for CMB Network Technology as an Algorithm Engineer. Cheney holds a master's degree from Nanjing University of Aeronautics and Astronautics.

Engineering

GraphRAG Explained: Enhancing RAG with Knowledge Graphs

GraphRAG is a new technique that augments RAG retrieval and generation with knowledge graphs.

Aug 02, 202416 min read

Engineering

How to Enhance the Performance of Your RAG Pipeline

This article summarizes various popular approaches to enhancing the performance of your RAG applications. We also provided clear illustrations to help you quickly understand these concepts and techniques and expedite their implementation and optimization.

Apr 14, 20249 min read

Engineering

Key NLP technologies in Deep Learning

An exploration of the evolution and fundamental principles underlying key Natural Language Processing (NLP) technologies within Deep Learning.

Mar 23, 202412 min read

Engineering

How to Evaluate RAG Applications

A comparative analysis of evaluating RAG applications, addressing the challenge of determining their relative effectiveness. It explores quantitative metrics for developers to enhance their RAG application performance.

Mar 23, 202413 min read

Engineering

Mastering LLM Challenges: An Exploration of Retrieval Augmented Generation

This four-part series handbook looks into RAG, exploring its architecture, advantages, the challenges it can address, and why it stands as the preferred choice for elevating the performance of generative AI applications.

Mar 22, 20246 min read

Engineering

OpenAI RAG vs. Your Customized RAG: Which One Is Better?

Comparing the performance of the OpenAI Assistants-enabled RAG system and the Milvus-powered customized RAG system.

Jan 05, 202410 min read

Engineering

Optimizing RAG Applications: A Guide to Methodologies, Metrics, and Evaluation Tools for Enhanced Reliability

Methodologies, metrics, and tools used to evaluate RAG applications.

Dec 29, 20238 min read

Engineering

Improving ChatGPT’s Ability to Understand Ambiguous Prompts

Prompt engineering technique helps large language models (LLMs) handle pronouns and other complex coreferences in retrieval augmented generation (RAG) systems.

Dec 12, 20237 min read