Title: I&#x27;ve built an advanced RAG (Retrieval-Augmented Generation) pipeline from scratch to demystify the complex mechanics of modern LLM-powered Question Answering systems. This repository features:<p>-- An implementation of a sub-question query engine from scratch to answer complex user questions.<p>-- Illustrative explanations that unveil the inner workings of the system.<p>-- An analysis of the challenges I faced while working with the system, like prompt engineering and cost estimation.<p>-- Qualitative comparison with similar frameworks like LlamaIndex, offering a broader perspective.<p>Key Takeaway: While Modern QA pipelines with advanced RAG abstractions may seem complex, they are fundamentally powered by a series of LLM calls with meticulous prompt design. Hoping that this repository provides intuitive insights for building more robust and efficient RAG systems. All feedback is warmly welcomed!
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