RAG

The authors present a deterministic 3‑Tiered Graph‑RAG architecture that outperforms a conventional vector‑based RAG pipeline on fact‑dense queries, reporting a 35 % reduction in hallucination rates as measured by the FactCheck‑Metric. The Graph‑RAG system leverages a hierarchical knowledge graph for retrieval, a rule‑based fusion layer, and a lightweight language model for final generation, achieving a 12 ms per‑query latency on a single A100 GPU. While the approach improves factual fidelity, it requires a pre‑built graph index and incurs higher storage overhead compared to flat vector indices. The benchmark demonstrates that Graph‑RAG can be a viable alternative when accuracy outweighs index‑building cost.

The article introduces a lightweight temporal reasoning layer for Graph‑RAG that annotates retrieved facts with freshness scores, enabling the system to prioritize up‑to‑date information over stale entries. By integrating a timestamp‑based decay function into the retrieval pipeline, the model can dynamically adjust relevance scores without altering the underlying graph structure, trading a modest increase in query latency for significantly higher answer accuracy in time‑sensitive contexts.

Amazon Bedrock’s new Knowledge Base feature, coupled with Amazon Quick, introduces real‑time, document‑level access control for enterprise RAG workflows. By querying the source system’s author metadata during each retrieval

Qlik deployed its Qlik Answers platform on Amazon Bedrock, enabling 40,000+ customers to receive grounded, sourced answers from both structured and unstructured enterprise data. The solution uses a layered, multi‑agent architecture that routes inference requests across regions and leverages Bedrock Guardrails to enforce data safety and compliance. The design balances global scale with trust, but requires careful configuration of cross‑region routing and guardrail policies to maintain latency targets.