Suggested Questions to ask Trusty

To get the most out of Trusty AI, use prompts tailored to your specific role and goals.

The following suggestions are categorized by role to help you get started.

Data Engineers

Use Trusty AI to perform rapid impact analysis and check the operational status of your pipelines without writing SQL or traversing lineage graphs manually.

• "Show me the upstream sources for the fct_monthly_sales table."

• "Are there any active incidents or failed monitors for the Snowflake production warehouse?"

• "List all downstream dashboards that will be affected if I modify the user_id column in the staging schema."

• "Identify the owner of the raw_transactions table."

• "Has data quality in dim_products changed between the last two profiles?"

• "Which columns in fact_orders would you recommend adding monitors for, based on the latest profile?"

• "Draw a lineage diagram for the monthly_sales table showing its upstream sources."

• "Tabulate the list of failed monitors in the Snowflake production warehouse to CSV."

Data Analysts

Use Trusty AI to discover relevant datasets for your reports and understand the quality of the data before you begin your analysis.

• "Find all datasets related to 'Customer Churn' in the marketing database."

• "Can you simplify my orders table lineage into logical business domains for easier understanding? Visualise it."

• "What is the null rate for the email column in the leads table?"

• "What charts are available in the Campaign Dashboard?"

• "Compare the row counts of stg_orders and fct_orders for the last 7 days."

• "What's the typical value range and distribution for order_amount in the fact_orders table?"

• "Is customer_id a reliable column to join on?"

• "Show how fact_orders relates to its dimension tables as a diagram."

• "Give me the null rates for the leads table so I can download them as CSV."

Data Governance Leads

Use Trusty AI to audit your documentation, track PII, and ensure that your data catalog aligns with your business glossary.

• "Which tables in the 'Sales' schema are missing descriptions?"

• "Identify all assets that are currently linked to the 'GDPR' business glossary term."

• "Show me a summary of data profiling results for the orders table to check for uniqueness constraints."

• "List all columns in the customers table that have been identified as potentially containing sensitive information."

• "Have null rates across the dim_customers table increased since the last profile run?"

• "Diagram which tables feed the Customer 360 dashboard."

• "List all assets linked to the GDPR term and tabulate them."

Tips for working with Trusty AI

To receive the most accurate information from Trusty AI, follow these guidelines:

• Use Backticks for Asset Names: When referencing a specific table, schema, or column, you enclose the name in backticks (e.g., `dim_users`) to help the model identify it as a technical asset that you're looking for.

• Be Context-Specific: Instead of asking "Show me monitors," try "Show me monitors in the Snowflake Reporting schema created in the last 30 days."

• Leverage Follow-up Questions: Trusty AI maintains session context. If you find a table, you can immediately ask a follow-up like "Now show me the profile statistics for that table."

• Verify Outputs: Trusty AI is an assistant designed to accelerate your workflow. For critical production changes, always verify the lineage or monitor status via the All Monitors or Lineage tabs in the Decube UI.