Moving from Legacy Topics to Dataset Q&A in Amazon Quick
Are you still using a legacy Topic for natural-language Q&A in Amazon Quick? This post explains how to upgrade to the new dataset enrichment experience, where business context (descriptions, synonyms, and instructions) lives directly on the dataset. The new approach unlocks access to the latest agentic models with improved answer accuracy.
This article is co-authored by Praney Mahajan and Amy Marvin
Are you using a legacy Topic for your natural-language Q&A experience today? There's a better way and it's available now.
With the new dataset enrichment experience, you can add business context (descriptions, synonyms, and instructions) directly to your dataset. This unlocks access to our latest agentic models with significantly improved answer accuracy. There is no legacy Topic required.
If you're working with multiple datasets, you can now create a multi-dataset Topic that lets you define joins and relationships, enabling accurate answers across your data. This is something that wasn't possible before.
What's changing?
Legacy Topics are now the previous generation of the Q&A setup experience. Going forward, the recommended approach is:
- Business context lives on the dataset not in a separate Topic configuration.
- Multi-dataset relationships are defined in the new multi-dataset Topic experience with support for natural-language join definitions.
By moving to the new model, you get access to stronger foundation models and improved answer quality immediately.
How to upgrade
If you have a single dataset in your legacy Topic
- Create a new dataset and enrich it. Migrate your business context (friendly names, field descriptions, synonyms, calculated fields, and instructions) directly onto the dataset.
- Your enriched dataset is ready for the latest Q&A experience, no Topic needed.
Here's what dataset enrichment looks like in practice : you can add descriptions, geospatial types, synonyms, and behavioral instructions directly on each field:
Figure-1 : Dataset enrichment in Amazon Quick : adding descriptions, synonyms, and instructions directly on a field
If you have multiple datasets in your legacy Topic
- Create new datasets : one per source and enrich each with its business context.
- Create a new multi-dataset Topic : define joins and relationships across your datasets, either manually or using natural language.
Here's what defining relationships looks like in the new multi-dataset Topic, the visual editor shows how your datasets connect:
Figure-2 : Multi-dataset Topic : Relationships tab showing datasets joined on Study Id
You can also add custom instructions to guide how the model reasons across your joined datasets:
Figure-3 : Multi-dataset Topic : Custom instructions tab with join logic and answering guidance
Why upgrade now?
| Benefit | Legacy Topic | Enriched Dataset + Multi-Dataset Topic |
|---|---|---|
| Model quality | Previous-generation models | Latest agentic models with stronger reasoning |
| Answer accuracy | Baseline | Significantly improved |
| Cross-dataset answers | Not supported | ✅ Define joins across datasets |
| Business context location | Locked in Topic config | Reusable, on the dataset itself |
| Natural-language joins | Not available | ✅ Describe relationships in plain English |
Prefer to automate?
You can create datasets and add metadata (descriptions, custom instructions) programmatically using the CreateDataSet API : see the
SemanticMetadata parameter for adding enrichment context directly via API.Enjoyed reading this content? Let the author know!
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