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When using Analytics Chat or other AI-powered features, the AI agent relies on your data model to understand your data. You can optimize your data model to help the AI generate more accurate queries and provide better insights. There are two ways to provide additional context to the AI:
  • Descriptions — visible to both end users and the AI agent.
  • AI context via meta — only visible to the AI agent, not exposed in the user interface.

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Using descriptions

The description parameter on cubes, views, measures, dimensions, and segments provides human-readable context that is displayed in the UI and also consumed by the AI agent. Use descriptions to clarify the meaning of a member for both your team and end users: 
cubes:
  - name: orders
    sql_table: orders
    description: All orders including pending, shipped, and completed

    measures:
      - name: total_revenue
        sql: amount
        type: sum
        description: Total revenue from completed orders only
        filters:
          - sql: "{CUBE}.status = 'completed'"

    dimensions:
      - name: status
        sql: status
        type: string
        description: "Current order status: pending, shipped, or completed"
Descriptions are a good starting point because they serve double duty — they help end users understand the data and also give the AI agent context for query generation.

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Using AI context

If you want to provide context to the AI agent without exposing it in the user interface, use the ai_context key inside the meta parameter. The meta parameter accepts custom metadata on views, measures, and dimensions.
ai_context must be defined on views or on individual members (measures, dimensions). ai_context defined at the cube level is not consumed by the AI agent.
Use ai_context on views to provide high-level guidance, and on individual members for member-specific instructions: 
views:
  - name: revenue_overview
    description: Revenue metrics and breakdowns
    meta:
      ai_context: >
        This is the primary view for revenue analysis. It combines
        order, product, and user data. Use this view when users ask
        about sales, revenue, or product performance.

    cubes:
      - join_path: order_items
        includes:
          - total_sale_price
          - count
          - status
          - created_at
      - join_path: order_items.products
        includes:
          - brand
          - category
For member-level context, define ai_context directly on the measure or dimension: 
cubes:
  - name: order_items
    sql_table: ECOMMERCE.ORDER_ITEMS

    measures:
      - name: total_sale_price
        sql: sale_price
        type: sum
        format: currency
        meta:
          ai_context: >
            Use this measure for any revenue-related questions.
            It includes all line items regardless of order status.

    dimensions:
      - name: created_at
        sql: created_at
        type: time
        meta:
          ai_context: >
            This is the order creation timestamp in UTC.
            For delivery analysis, use delivered_at instead.
You can also override member-level ai_context when including members in a view — for example, to define synonyms or acronyms that only apply in the context of that view: 
views:
  - name: sales_overview
    description: Sales metrics and breakdowns
    meta:
      ai_context: >
        This view is for sales performance analysis across brands.

    cubes:
      - join_path: order_items
        includes:
          - total_sale_price
      - join_path: order_items.products
        includes:
          - name: brand
            meta:
              ai_context: >
                Common acronyms: LC = Lucky Charms,
                HNC = Honey Nut Cheerios.

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Descriptions vs. AI context

descriptionmeta.ai_context
Visible in the UIYesNo
Used by the AI agentYesYes
Supported onCubes, views, measures, dimensions, segmentsViews, measures, dimensions
Use description when the context is useful to both end users and the AI agent. Use ai_context when you want to provide additional instructions or context that is only relevant to the AI agent — for example, guidance on which measures to prefer, nuances about data quality, or business logic that would be confusing in a user-facing description. You can use both together. The AI agent reads both the description and ai_context when generating queries: 
cubes:
  - name: order_items
    sql_table: ECOMMERCE.ORDER_ITEMS
    description: Line items for all orders

    measures:
      - name: total_sale_price
        sql: sale_price
        type: sum
        format: currency
        description: Total revenue across all line items
        meta:
          ai_context: >
            This is the primary revenue metric. Always use this
            instead of summing the sale_price column directly.
            When users ask about "sales", they mean this measure.

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Best practices

  • Add descriptions to all public members. Descriptions help both end users and the AI agent understand your data model.
  • Use AI context for agent-specific guidance. If you need to tell the AI agent which measure to prefer or how to interpret ambiguous terms, use ai_context.
  • Define context on views or individual members. ai_context defined at the cube level is not consumed by the AI agent. Place it on the view itself or on individual measures and dimensions.
  • Be specific. Vague context like “important metric” is less helpful than “use this measure when users ask about monthly recurring revenue.”
  • Document relationships. Use AI context to explain how cubes relate to each other and which views to prefer for common questions.
  • Keep it up to date. As your data model evolves, update descriptions and AI context to reflect the current state.