In this article, you will learn how to design, assemble, and tune a retrieval-augmented generation system that runs entirely on a standard laptop, without cloud infrastructure or paid APIs.
Topics we will cover include:
How quantization, compact embedding models, and in-process vector stores make a full RAG pipeline possible on consumer hardware.
Which lightweight packages handle each stage of the pipeline, from document ingestion and chunking through retrieval, prompting, and local generation.
How to make the system reliable through source citations, retrieval thresholds, evaluation sets, and query logs that distinguish retrieval failures from generation failures.
