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Contract intelligence across hundreds of documents exposes RAG’s top-k limits
The excerpt describes how chunk-based RAG retrieves only the top-k passages, which can miss answers requiring information across many contracts. The platform case study is relevant to designing document systems for cross-record questions.
- What
- The platform helps answer questions across large contract portfolios, such as total contract value and upcoming expirations.
- How
- It uses AI agents to extract contract data, a multi-model approach to verify accuracy, and embedded analytics and natural language querying.
- Why it matters
- Top-k RAG can miss portfolio-wide answers because aggregation requires data across the full dataset, not just a few relevant chunks.
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