Structured Document Comparison: Going Beyond Naive RAG

Author(s): Angela & Kezhan Shi

Originally published on Towards AI.

From Precise Extraction to Transparent Justification using Advanced Methods

In this article, I will present a tool that automatically extracts and compares answers from multiple documents based on a series of questions. These answers are presented in a table with their sources clearly annotated.

This comparator is designed to meet various concrete needs, such as:

Compare insurance contracts to evaluate the differences in guaranteesAnalyze financial reports by extracting and comparing key indicatorsReview CVs to extract relevant skills or experiencesCheck invoices by quickly identifying essential information

Naive RAG approaches suffer from several limitations, notably in the ability to segment texts correctly (chunking), has accurately search for answers and to convincingly justify the results obtained.

To overcome these obstacles, we have developed innovative solutions which improve the precision, robustness and explainability of the responses generated.

In this article, we will explore these solutions and understand how they allow you to go beyond traditional RAG methods, guaranteeing results clear, justified and usable by the professions.

The RAG approach, although recognized, presents several specific challenges linked to the variability of the information sought.

As part of our RAG approach, we have identified three major challenges which directly impact the quality of the results obtained.

The chunking, that is to say cutting the text into usable segments, poses a real challenge depending… Read the full blog for free on Medium.

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Published via Towards AI