Demystifying Advanced RAG Pipelines

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I'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: -- An implementation of a sub-question query engine from scratch to answer complex user questions. -- Illustrative explanations that unveil the inner workings of the system. -- An analysis of the challenges I faced while working with the system, like prompt engineering and cost estimation. -- Qualitative comparison with similar frameworks like LlamaIndex, offering a broader perspective. 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!

Visit product View on Hacker NewsOct 28, 2023

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