Posts by Lin Sun
From Ingestion to Inference: RAG Pipelines on AMD GPUs
- 02 October 2025
Retrieval-Augmented Generation (RAG) is a machine learning architecture that enhances Large Language Models (LLMs) by combining generation with information retrieval from external sources. It was introduced to address the limitations of traditional LLMs by allowing them to access and utilize up-to-date information from internal and/or external knowledge bases. When a query is received, RAG first retrieves relevant documents or information from its knowledge bases, then uses this retrieved context alongside the query to generate more accurate and informed responses. This approach helps reduce hallucinations (making up information) common in standard LLMs, while also enabling the model to access current information not present in its original training data. RAG has become particularly valuable in enterprise applications, such as customer support systems, research assistants, and documentation tools, where accuracy and verifiable information are crucial.
Coding Agents on AMD GPUs: Fast LLM Pipelines for Developers
- 30 September 2025
The rapid rise of AI-assisted development is transforming how software is built, with coding agents emerging as powerful tools for modern developers. In this blog, we will show you how to deploy coding agents on AMD GPUs using frameworks such as SGLang, vLLM, and llama.cpp, and walk through a practical workflow example: creating a Minesweeper game using Aider.