Beyond RAG: Task-aware knowledge compression for enterprise AI on AWS

This article introduces task-aware knowledge compression (TAKC), a technique that addresses RAG limitations by pre-compressing entire knowledge bases into task-specific representations deployed on AWS for complex analytical queries spanning hundreds of documents.

• TAKC compresses documents offline through task-specific lenses, preserving cross-document connections that similarity search misses
• Multi-rate compression maintains four fidelity tiers (8x to 64x reduction) with query complexity analyzer routing questions to appropriate tier
• Serverless architecture uses Lambda for compute, ElastiCache Serverless for caching, Bedrock for compression/inference, and Cognito for authentication
• Ingestion pipeline chunks documents and compresses at all four tiers; query pipeline retrieves pre-compressed context from cache
• Cost comparison shows TAKC Medium tier at 6.25% of full context cost versus RAG's 10% for top-10 chunks
• TAKC suits cross-document reasoning over stable knowledge bases; RAG better for narrow lookups and frequently changing data
• Reference implementation available at aws-samples/sample-bedrock-takc-compression with CDK deployment

TAKC enables enterprise AI systems to perform complex analytical reasoning across large document collections while reducing token costs and improving response quality compared to traditional retrieval approaches.