Gemini 2.0 vs. Agentic RAG: Who wins at Structured Information Extraction?

The blog post explores the effectiveness of two approaches, Gemini 2.0 Flash and Agentic Retrieval-Augmented Generation (RAG), for parsing and extracting information from SEC S-1 filings, which are lengthy and complex documents submitted by companies before going public. The study, using a dataset of 1,200 filings, found that while RAG was generally more effective and cost-efficient in extracting most fields, Gemini 2.0 excelled in extracting information that required a broader understanding of the entire document. RAG proved to be significantly cheaper and used fewer tokens compared to the long context approach of Gemini 2.0. The hybrid method of using both approaches was suggested as optimal, with RAG handling straightforward extractions and Gemini 2.0 dealing with fields requiring comprehensive document context.

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