A developer built and benchmarked three question-answering systems: RAG, GraphRAG, and Agentic GraphRAG, using a corpus of 2,951 documents related to the Olympic Games. The Agentic GraphRAG system outperformed the others, achieving 2.24 times better results than standard RAG and 1.24 times better than single-shot GraphRAG, while also demonstrating a lower failure rate. The project highlighted that traditional RAG struggles with questions requiring aggregation across multiple documents, as its semantic search focuses on similarity rather than structured data retrieval. AI
IMPACT Demonstrates advancements in retrieval-augmented generation techniques for more accurate and robust question-answering systems.
RANK_REASON Developer's personal project detailing the implementation and benchmarking of different RAG techniques. [lever_c_demoted from research: ic=1 ai=1.0]
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