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!
-- 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!