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

<p>If you’re using <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/jumpstart-foundation-models-customize-rag.html" target="_blank" rel="noopener">Retrieval-Augmented Generation</a> (RAG) for complex analytical tasks that span hundreds of documents, such as financial due diligence or regulatory compliance reviews, you’ve likely hit its ceiling. Similarity search surfaces relevant fragments but often misses cross-document connections. This post shows you how to address that gap using task-aware knowledge compression (TAKC), a technique that pre-compresses entire knowledge bases into task-specific representations deployed on AWS. You can deploy a complete open-source implementation in your own account.</p> <h2>Task-aware knowledge compression</h2> <p>Consider a private equity firm evaluating a $500 million acquisition of a manufacturing company. The due diligence team must analyze financial statements spanning 12 subsidiaries and 5 years. They also face 200+ supplier contracts, environmental compliance reports from 8 facilities, and 50+ legal cases. When an analyst asks about consolidated financial risks given current supplier terms and pending litigation, RAG’s similarity search cannot surface that response. Hundreds of documents hold the relevant information, and the connections between them share no lexical similarity.</p> <p>TAKC addresses this type of problem by using an LLM to produce shorter, task-focused summaries of documents, with different summaries for different tasks.</p> <p>Different tasks require different information from the same document. An annual report compressed for financial analysis needs revenue figures, margins, and cash flow data. The same report compressed for a compliance review needs regulatory citations and violation histories. Generic summarization attempts to cover everything, which dilutes the information density for any specific use case. TAKC compresses documents through the lens of a specific task, keeping what matters and discarding the rest. The Ingestion pipeline section shows how the compression prompt specifies exactly what information to preserve. For production deployments, store task-type prompts in a versioned configuration (such as AWS Systems Manager Parameter Store or a dedicated Amazon Simple Storage Service (Amazon S3) prefix) so that prompt changes are auditable and recompression can be triggered when prompts are updated.</p> <p>The system compresses documents offline, once per document per task type. At query time, the system retrieves a pre-compressed representation rather than the original document. The system then answers questions using the compressed version instead of the full document. If the compressed representation lacks sufficient detail, the query complexity analyzer routes the question to a lower compression tier that retains more context.</p> <p>TAKC provides access to the <em>entire</em> knowledge base in compressed form, not just the top-k chunks that a similarity search returns. The system preserves connections between documents because the compression sees documents together. It also produces different compressed outputs for different tasks from the same source material. The financial compression of a 10-K filing looks nothing like the legal risk compression of that same filing. The compression reduces token count by 8x to 64x while targeting task-relevant information for retention.</p> <h2>Multi-rate compression</h2> <p>Different queries require different levels of fidelity. A question such as “What was Q3 revenue?” needs far less context than a request to analyze relationships between supplier payment terms and quarterly cash flow across subsidiaries.</p> <p>TAKC addresses this by maintaining four compression tiers for each task type. At the lightest compression (8x), the system retains approximately 87.5% less context. It preserves enough detail for multi-step reasoning and cross-document synthesis. At the medium tier (16x), context reduct