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AI context allows you to enhance AI understanding by providing additional information about your organization, data sources, schemas, tables, and columns. This helps the AI generate more accurate and relevant responses when creating charts, reports, or answering questions in chat.

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Automatic AI context

Basedash automatically provides the AI with comprehensive context about your data sources, including:
  • Data source structure: Table names, column names, and data types from all connected databases and warehouses
  • Table and column descriptions: Any existing comments or descriptions from your source databases (PostgreSQL and Snowflake comments, BigQuery descriptions)
  • Semantic layer context: If you use a semantic layer like dbt, Basedash includes any documentation, descriptions, and metadata defined in your dbt models
This automatic context ensures the AI has a foundational understanding of your data structure without requiring any manual setup.

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How AI interprets your data

Basedash performs automatic pre-processing when you connect your data sources to understand the structure of your schemas, tables, and columns. The AI can analyze data types, relationships, and basic metadata to generate queries and visualizations. However, custom context provides the business intelligence that goes beyond the technical structure. It helps the AI understand:
  • How your organization operates and what metrics matter most
  • What your data actually represents in business terms
  • Internal terminology and KPIs specific to your company
  • The context and meaning behind complex data structures
This additional layer of understanding enables the AI to generate more relevant, business-focused insights rather than just technical queries.

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How it works

Custom context is automatically considered by the AI whenever you create charts, chat, or reports in Basedash. Global context applies to every conversation within your organization, while schema and column-level context is used when that specific data is referenced by the AI.

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Accessing custom context

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Global context

Global context applies to all AI interactions in the organization. You can access global context through the organization dropdown in the top-left corner. Organization dropdown with global context option

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Context in chat

You can also add context directly from the chat interface. Look for the “Global context” button (asterisk icon) in the top-left of the chat input. Chat interface with add context button

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Data source context

You can also add context tied to a specific data source, schema, table, or column.
  1. Navigate to the Data page
  2. Right-click on a data source, schema, table, or column
  3. Select “Edit AI context”
Data page with edit AI context option

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Best practices

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Start with global context

We recommend beginning with global context as one of your first setup steps. This provides the AI with fundamental understanding of your business, terminology, and key metrics.

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Define internal terminology

Global context is a great place to define internal terminology. This helps the AI understand your business, terminology, and key metrics. Use custom context to define:
  • Internal KPIs: Specific metrics that only your organization understands
  • User terminology: How you refer to different user types (e.g., “signups,” “active users,” “premium customers”)
  • Business jargon: Company-specific terms and definitions
  • Metric definitions: Custom calculations or business logic for specific metrics
Once defined, the AI will understand and use your terminology consistently across all interactions.

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Getting started

We recommend adding custom context as part of your initial Basedash setup. This ensures the AI has the right context from the beginning, leading to more accurate and relevant responses. For step-by-step guidance, see our getting started guide which includes custom context setup as part of the recommended workflow.

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Examples

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Organization context

Our company is a SaaS platform for e-commerce businesses.
We refer to our customers as "merchants" and their customers as "shoppers."
"MRR" refers to Monthly Recurring Revenue from subscription plans.
"Churn" means when a merchant cancels their subscription.

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Schema context

The "analytics" schema contains event tracking data from our platform.
The "billing" schema contains subscription and payment information.
The "support" schema contains customer support ticket data.

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Column context

The "metadata" JSON column contains user preferences and settings.
The "status" column uses values: "active", "suspended", "cancelled".
The "created_at" timestamp is in UTC timezone.