Amazon Quick added a generative AI tool that converts natural‑language prompts and selected datasets into a native Analysis that can be refined and published as a dashboard.
Amazon Quick introduced a generative AI feature that turns natural‑language prompts and selected datasets into complete, production‑ready Analyses that can be published as dashboards. Rather than manually placing visuals, filters, and calculated fields, users describe the insights they need and the service proposes a structured Analysis with multiple sheets and recommended components for review before generation. The generation flow begins by selecting one to three datasets inside Quick. Those datasets can connect to sources such as Amazon Redshift, Amazon S3, or uploaded files, and users may choose multiple related tables — for example, orders in one dataset and products in another. An Add data option lets authors include additional datasets before sending the prompt.
When a prompt is submitted, Quick inspects dataset structure and column statistics and reports real‑time progress while it analyzes columns and builds an analysis plan. The product’s Analysis surface is the usual authoring environment for visuals, filters, and calculated fields; the new capability automatically creates those components and produces a plan that maps how sheets and controls would be organized.
A two‑pane plan view helps authors evaluate the proposed layout before committing. The left pane displays the original prompt and a concise dataset summary; the right pane shows the proposed structure, including filter controls, sheets, and planned visuals. Authors can choose Generate immediately or Edit the plan to rename sheets, add or remove visuals, and reorganize layout. During generation the UI visualizes each component being created sequentially, including calculated fields and filters, so authors can follow and adjust work in progress.
The feature was available in early access to enterprise users and requires an AWS account plus an Amazon Quick Enterprise Edition subscription. Early access authors across operations, engineering, and data science reported substantial time savings. One tester, Jeff Sondic, Pre‑Construction Manager at GES Ops Construction (Amazon), said: “The results are awesome and there is no comparison in the time it takes AI to perform analysis and create dashboards vs. a human being.
Output is a native Quick Analysis that authors can refine within the authoring surface and publish as a dashboard with a single click. The tool targets recurring operations reports, leadership reviews, and exploratory analysis, producing practical artifacts — visuals, filters, and calculated fields such as total revenue, average order value, and month‑over‑month growth — so builders can move from dataset to production‑ready dashboard in minutes rather than hours.
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