Improve contract search accuracy with auto-generated filters in Amazon Bedrock

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Enterprises rely on large volumes of complex legal agreements to make critical business decisions — determining rights, renewal options, geographic restrictions, and compliance obligations. In industries like entertainment and media, where organizations manage thousands of contracts across multiple jurisdictions, this work remains largely manual: time-consuming, costly, and difficult to scale. Our AI-Driven Annotation (AIDA) solution, powered by AWS, helps address this challenge by transforming unstructured contracts into searchable, actionable intelligence. AIDA enables users to ask natural-language questions across large contract repositories — but delivering precise answers requires more than semantic search alone. Legal documents are highly contextual, and retrieval-augmented generation (RAG)-based systems can surface more content than a language model can effectively process. Without careful control over which excerpts are retrieved, and without sufficient document-level context, important clauses risk being overlooked or misinterpreted. 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 accuracy. Solution overview The reference architecture diagram represents how AIDA uses a RAG architecture built on Amazon Bedrock …

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