Build an AI Data Analysis Agent for Business Metrics
Design an AI agent that connects to a database, answers natural language questions about business data, generates charts, and explains insights.
Enable non-technical team members to query business data conversationally without writing SQL or using BI tools.
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Prompt objective
Enable non-technical team members to query business data conversationally without writing SQL or using BI tools.
Real use case
A marketing team of 8 people constantly asks the data team for reports. Simple queries take 2 days to get answered. An AI agent could handle 80% of ad-hoc questions instantly.
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Prompt
Act as a data engineering and AI architect. Design an AI data analysis agent for [COMPANY NAME] that provides natural language access to [DATABASE TYPE] data.\n\n**Context:**\n- Database: [POSTGRES/BIGQUERY/SNOWFLAKE] with [NUMBER] tables\n- Primary users: [TEAM NAME] ([NUMBER] people) with no SQL knowledge\n- Common questions: revenue trends, campaign performance, cohort analysis, [OTHER]\n- BI tool in use: [LOOKER/TABLEAU/METABASE]\n- Data refresh frequency: [HOURLY/DAILY/REAL-TIME]\n\n**Deliverables (numbered):**\n1. Agent architecture: NL-to-SQL pipeline with schema awareness, query validation, execution sandbox, result formatting, and chart generation\n2. Schema understanding: how the agent learns table structures, relationships, business definitions (metric glossary), and common query patterns\n3. Query safety: read-only access, query timeout limits, row limit ([NUMBER] max), prevention of expensive full-table scans, and approval for complex queries\n4. Response format: natural language summary of findings, data table (top [NUMBER] rows), suggested chart type with generated visualization, and follow-up question suggestions\n5. Error handling: graceful responses for ambiguous questions, schema mismatches, empty results, and query timeouts with helpful guidance\n6. Query logging and learning: store all queries, track which answers users found helpful, build a query pattern library for common questions\n7. Integration: embed in [SLACK/TEAMS/WEB APP], support file upload for CSV analysis, and export results to [FORMAT]\n\n**Constraints:**\n- Must never allow write/delete operations\n- Must cite data source table and freshness timestamp in every response\n- Must flag when data may be incomplete (e.g., current month not yet closed)
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- 1Replace the key placeholders first: COMPANY NAME, DATABASE TYPE, POSTGRES/BIGQUERY/SNOWFLAKE, NUMBER.
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