Building a Transformer Paper Q&A System with RAG and Mastra
Published:Jan 8, 2026 08:28
•1 min read
•Zenn LLM
Analysis
This article presents a practical guide to implementing Retrieval-Augmented Generation (RAG) using the Mastra framework. By focusing on the Transformer paper, the article provides a tangible example of how RAG can be used to enhance LLM capabilities with external knowledge. The availability of the code repository further strengthens its value for practitioners.
Key Takeaways
- •Article demonstrates RAG implementation with Mastra framework.
- •Focuses on the Transformer "Attention Is All You Need" paper.
- •Provides a GitHub repository with sample code.
Reference
“RAG（Retrieval-Augmented Generation）は、大規模言語モデルに外部知識を与えて回答精度を高める技術です。”