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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, automations, 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 automations in Basedash. Global context applies to every conversation within your organization. You can also add group context to control how the AI works for different teams. Data source, schema, table, 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 every AI interaction in the organization. Admins edit it in Settings → AI context, in the Global context section. Include information about your company, how your data is structured, or how you calculate metrics. Changes save automatically. Admins can also ask chat to update global context for them.

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Skills

Skills are reusable sets of instructions that agents load only when they’re relevant, such as how to run a retention analysis or how the finance team defines revenue. Unlike global context, which is always loaded in full, each agent sees the list of skills and reads a skill’s instructions on demand. Admins manage skills in Settings → AI context. See Skills for how to create skills, examples, and limits.

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Group context

Group context applies to members of a specific group. This is useful when different teams need the AI to behave differently, such as:
  • Finance: Explain reporting conventions and preferred accounting terminology
  • Support: Explain ticket lifecycle, common issue categories, and internal abbreviations
  • Sales: Define pipeline stages and how to interpret deal status fields
To edit group context, an admin opens Settings → Groups, clicks a group, and edits its AI context field. See Groups.

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Personal context

Personal context applies only to you, in the current organization. Use it for preferences, shorthand, or recurring instructions, like “Show currency in EUR” or “Keep summaries to three bullet points.” To edit it, open Settings → Profile and fill in Personal AI context. You can also ask chat to remember a preference, and it updates your personal context for you.

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Context in chat

In the chat input, click the + button. Admins see an AI context option that opens the organization’s AI context settings. Everyone can upload files to give the AI extra context for that chat.

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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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Limits

Global context and data source context can each be up to 50,000 characters. Group context, personal context, and insights context can each be up to 20,000 characters.

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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 explain:
  • Internal KPI terminology: How your organization names and discusses important metrics
  • User terminology: How you refer to different user types (e.g., “signups,” “active users,” “premium customers”)
  • Business jargon: Company-specific terms and definitions
Use Models for the executable calculations and business logic behind metrics. Once both are in place, the AI can understand your terminology and apply the same modeled logic across 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

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Schema context

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Group context

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Column context