Large language models are trained on public code, docs, and synthetic examples. They have zero visibility into your company’s private database, naming rules, or business logic. Without that context, the model hallucinates table names, mismatches data types, and applies generic join patterns that do not reflect your actual relationships.
Even if you paste snippets of DDL, the model only sees the tables you share in the prompt window. It still lacks lineage, indexes, row counts, or semantic intent like “active_user.”
Modern warehouses contain hundreds of tables and thousands of columns-well beyond the token limit of a single prompt.
Teams abbreviate, alias, or version tables in unique ways (e.g., acct_txn_v2). These patterns rarely exist in the model’s pre-training data.
Sprint-driven schema changes mean yesterday’s correct answer may break today. Static model knowledge quickly goes stale.
1. Paste the exact CREATE TABLE statements or describe columns before asking for a query.
2. Include primary/foreign keys so the model can infer join paths.
3. Specify the dialect (PostgreSQL, Snowflake, MySQL) to avoid syntax mismatches.
4. Ask for incremental refinement: first draft, then optimization, then edge-case checks.
Galaxy connects to your database and feeds the AI real-time schema metadata-table names, columns, keys, and statistics-so it generates SQL that actually runs.
Store and share verified SQL snippets. The copilot can reference these trusted patterns, reducing hallucinations.
Every AI-generated query is saved with run history, making it easy to trace errors and roll back.
Teams using Galaxy report 3-4× faster query authoring and 50% fewer syntax errors compared with raw ChatGPT prompts.
• Keep prompts short but complete: provide the minimum tables and relationships required.
• Use natural language tests: “Return zero rows if count is negative.” The model will add sanity checks.
• After generation, run EXPLAIN plans in Galaxy to catch performance issues before production.
• Promote final, corrected queries to Galaxy Collections so future prompts inherit the right patterns.
ChatGPT’s mistakes stem from missing context, not flawed reasoning. Feed the model authoritative schema data or use a tool like Galaxy that supplies it automatically, and your SQL success rate jumps dramatically.
How to fix ChatGPT SQL errors; ChatGPT schema aware SQL; AI SQL copilot best practices; Galaxy AI SQL accuracy
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