Improve contract search accuracy with auto-generated filters in Amazon Bedrock
In this post, we describe how AIDA works at a high level and how it helps address these challenges — grounding users in the right contracts, under the right legal context, and within the right access boundaries. Specifically, we explore how AIDA uses implicit and explicit filtering, along with metadata-enriched chunking in Amazon Bedrock Knowledge Bases, to dramatically improve contract search…
Enterprises managing vast collections of intricate legal agreements face significant challenges in extracting actionable insights from their extensive contract repositories. Traditional methods of manual review are both time-consuming and costly, limiting scalability and efficiency. Amazon Bedrock's AI-Driven Annotation (AIDA) solution addresses this issue by converting unstructured contracts into searchable, intelligent data.
AIDA empowers users to pose natural-language queries to large contract archives, but to deliver accurate answers, it must go beyond mere semantic search. The solution employs advanced filtering techniques—both implicit and explicit—to ensure that retrieved information is both relevant and contextually appropriate. Implicit filtering automatically applies predefined metadata constraints—such as specific parties, dates, or jurisdictions—before conducting semantic search, thereby narrowing the search space and enhancing precision.
Explicit filtering allows users to further refine results through direct input. By integrating these filtering mechanisms with Amazon Bedrock Knowledge Bases, AIDA ensures that users are guided to the most pertinent clauses within the appropriate legal context, thereby improving contract search accuracy and operational efficiency.
Written by urgent.news from AWS Machine Learning's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
