Schema Mapping (Semantic Context)
ConceptSchema mapping is the process of providing explicit semantic definitions, relationships, and business logic for database tables and columns so users and AI agents query them correctly.
What is Schema mapping, in plain English?
Think of schema mapping as a bilingual dictionary between human business terms and cryptic database column names. To a human, "revenue" is simple. But in a database, is revenue in `orders.total_price`, `invoices.subtotal`, or `transactions.gross_amt`? Schema mapping teaches query tools and AI models exactly what each column means and how tables connect.
Schema mapping example
Mapping the column `rev_usd_net` in the `fact_sales` table as the primary source for "Company Net Revenue (excluding discounts and sales tax)".
Common Schema mapping mistakes
Writing correct SQL syntax is easy for AI; knowing which of five similar tables contains the true financial metric is the hard part. Without proper schema mapping, AI tools will write syntactically perfect queries that calculate completely wrong numbers.
- Seven Questions to Ask Before You Trust an AI's Answer About Your Data
- How We Keep an LLM From Inventing a Column Name
- Semantic Layers vs. Prompting: Where Business Definitions Should Live
- Joining ad spend to orders with no common key
- 15 prompts marketers actually reuse (copy-paste library)
- Reasoning models vs fast models: which questions deserve “thinking”?
Last reviewed August 28, 2026.