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 accuracy.
Se muestra el contenido original; la traduccion localizada aun no esta disponible.
Qué ocurrió
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.
Por que importa
The proceeding may create legal precedent, financial exposure or operating constraints for Amazon.
Entidades afectadas
Ver evidencia
8 articulos · 1 informe original · 1 independientes
- AWS Machine Learning BlogFuente primaria · Respalda · EN · 52%Improve contract search accuracy with auto-generated filters in Amazon Bedrock ↗
- AWS Machine Learning BlogFuente primaria · Respalda · EN · 49%Implement vector-prompt document classification using Amazon Bedrock ↗
- AWS Machine Learning BlogFuente primaria · Respalda · EN · 53%Amazon Bedrock AgentCore payments is now generally available: Enabling agents to transact safely and autonomously at scale ↗
- AWS Machine Learning BlogFuente primaria · Respalda · EN · 50%Asynchronous patterns for calling Amazon Bedrock AgentCore agents in serverless pipelines ↗
- AWS Machine Learning BlogFuente primaria · Respalda · EN · 50%Build intelligent security for healthcare APIs with Amazon Bedrock ↗
- AWS Machine Learning BlogFuente primaria · Respalda · EN · 79%Build a no-code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 3: Visualizing insights with Amazon Quick Sight ↗
- AWS Machine Learning BlogFuente primaria · Respalda · EN · 77%Build a no-code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 2: Data preparation and model building with Amazon SageMaker Canvas ↗
- AWS Machine Learning BlogFuente primaria · Respalda · EN · 100%Build a no-code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 1: Setting up your Snowflake environment ↗
Afirmaciones
- Improve contract search accuracy with auto-generated filters in Amazon Bedrock Observado
Conflictos
No se detectaron conflictos importantes en la evidencia disponible.
Cronología
- Primera publicación
- Fuente primaria · 56/81%
- Fuente primaria · 56/81%
- Fuente primaria · 56/63%
- Fuente primaria · 54/63%
- Fuente primaria · 71/60%
- Fuente primaria · 66/60%
- Fuente primaria · 66/60%
Movimiento del mercado posterior al evento
La reacción del mercado aún no está disponible para este activo y periodo.