Tutorial Teaches Building Naive, Advanced and Modular RAG Systems in Python

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A DEV Community tutorial walks developers through building Retrieval-Augmented Generation (RAG) systems from scratch in Python, progressing from naive to advanced and modular designs. It explains that RAG addresses LLM limitations such as hallucinations and outdated knowledge by supplying retrieved external documents as context for answers. The workflow covers chunking documents, converting them to vectors, indexing, retrieving relevant chunks, and passing them to a language model. The author uses a Spider-Man story document as a simple running example to demonstrate question answering over external data. The tutorial also covers concepts such as query rewriting, reranking, routing and query fusion, with an accompanying video.

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