-- 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!