How to Build a Robust RAG System with Minimal Resources

A guide to building a retrieval-augmented generation system that runs entirely on a laptop without cloud infrastructure or paid APIs, using quantized models (GGUF), compact sentence-transformer embeddings, and file-based vector stores like FAISS or ChromaDB. It walks through document chunking, embedding, retrieval techniques like query expansion and HyDE, local generation with llama.cpp or Ollama, and reliability practices such as source citations, similarity thresholds, evaluation sets, and query logging to distinguish retrieval failures from generation failures.

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