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Validio Recommendations use AI and ML to automatically analyze your data sources and suggest pre-configured validators, streamlining the setup of comprehensive data monitoring.
The Validators tab of a source with the AI recommendations action available

Configure Validators with AI recommendations

Setting up comprehensive data monitoring can be time-consuming when configuring validators individually. Recommendations streamline this process by automatically analyzing your data source and suggesting appropriate validators tailored to your schema and data patterns. Validio offers two recommendation modes:
  • AI recommendations: Uses LLM-powered analysis for context-aware suggestions (requires LLM credentials).
  • ML recommendations: Uses statistical machine learning and heuristics
Both modes allow you to profile your data and review existing resources to suggest pre-configured windows, filters, and segmentations alongside validators. You can then select which recommendations to apply, creating multiple validators and resources at once.

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Prerequisites for AI recommendations

To generate recommendations using LLMs, ensure that you have the following:
  • Enabled the Allow LLM credentials setting in your Validio Workspace. See Managing Workspace Settings.
  • Appropriate permissions to create credentials (credentials:WRITE permissions for the Namespace). See Managing Roles.
  • At least one LLM credential configured in your environment. See LLM Credentials.

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Generating recommendations

On the Validators tab for a configured Source,
  1. Click AI recommendations (or New recommendations if you do not have an LLM credential).
    The recommendations dialog with data profiling, guidance notes, and LLM credential options

    AI recommendation options

  2. (Optional) In the dialog window, toggle Use data profiling. Profiling increases the accuracy of the recommendations.
    • Sampling Percentage: (Only available for tables and materialized views.) Use the slider or enter a number to adjust the percentage of data to include in profiling. A higher sampling percentage results in more accurate results, but reads more data and takes longer to complete.
    • Data Scope: (Recommended) Cap the execution time on large datasets by limiting the fields and number of rows used for profiling:
    • Lookback Filter: (Recommended) Limit the data volume scanned by targeting only a specific window of recent data:
  3. (Optional) Add Guidance notes to provide domain knowledge or business context, such as key metrics to monitor, data quality priorities, or specific fields to focus on.
  4. Choose an LLM credential or Use Statistical ML (the default, if you do not have an LLM credential).
  5. Click Run.
    • Click Processing to view a progress checklist as the system and model gathers information and generates recommendations. The process runs in the background, so you can navigate away while it completes.
    • To cancel a processing recommendation, click Cancel.
    The recommendations progress checklist showing each step as the agent works

    The recommendations agent runs in the background, but you can view its progress

  6. Click View recommendations to see the list of pre-configured Validators as well as relevant Windows, Filters, and Segmentations. The Requires column indicates dependencies between the resources.
    The generated recommendations list with validators, windows, filters, and segmentations and their dependencies

    Generated recommendations list

  7. Check each of the resources that you want to apply to your source. If you uncheck one resource (such as a segmentation), it will automatically uncheck all validators that require that resource.
  8. Click Apply selected to create the recommended validators. If you are creating many validators at once, this may take a few minutes to process. Once they are created, the validators will automatically backfill with historical data. It may take a few minutes for the data to populate while this initial sync completes.