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Baseline Enterprise RAG, From PDF to Highlighted Answer | Towards Data Science

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fastest way to understand what RAG is is to build the smallest version that actually works, run it on a real document, and look closely at what just happened.

That’s this article. About a hundred lines of Python (no vector database, no framework, no agents) running on the Attention Is All You Need paper (Vaswani et al. 2017; arXiv non-exclusive distribution license, declared on the arXiv abstract page ), returning a sourced answer with the exact source lines highlighted on the page.

Then we walk back through each block and ask the question it naturally raises. Each question is what a later article develops.

The minimal pipeline is the smallest amount of code that respects the four bricks and produces a verifiable answer. Every later article adds capability the team needs after a specific failure on real documents, not because the architecture needed more layers.

1. What we’re building

The pipeline has four bricks (Part II goes into each one in detail) plus a final, optional rendering step.…

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