Contribution · Application — Software

AI for Database Query Assistants

Every data team gets hundreds of ad-hoc queries that consume analyst time. Text-to-SQL LLMs grounded in schema metadata and a semantic layer now handle maybe 60-80% of these. The working pattern: LLM drafts SQL, runs it in read-only sandbox, explains the result, offers visualization. The real value isn't the syntax — it's the semantic layer that tells the LLM what 'active customer' actually means in this company.

Application facts

Domain
Software
Subdomain
Analytics
Example stack
Claude Opus 4.7 or GPT-5 for SQL drafting · Semantic layer (dbt, Cube.dev, LookML) · Vanna.ai or in-house RAG for schema/docs retrieval · Read-only service account with row-level security · Query audit + cost budget

Data & infrastructure needs

  • Database schema (DDL, foreign keys, comments)
  • Semantic layer or metric definitions
  • Sample queries with labeled intent
  • Row-level security policies

Risks & considerations

  • Wrong SQL producing wrong numbers confidently
  • Security — leaking PII by querying sensitive tables
  • Runaway queries that burn warehouse credits
  • Prompt injection via table comments or data contents
  • Compliance — audit trail for every query touching regulated data

Frequently asked questions

Is AI text-to-SQL safe?

Yes, with governance: read-only database role, row/column-level security, query cost caps, and a semantic layer that defines metrics once. Always show the generated SQL, not just the result. And log every query for audit.

What LLM is best for text-to-SQL?

Claude Opus 4.7 and GPT-5 both write strong SQL. For self-hosting, fine-tuned Code Llama or DeepSeek-Coder variants are capable. The biggest factor is quality of schema docs and semantic layer, not the model.

Regulatory concerns?

DPDPA/GDPR for PII, SOX for financial data, HIPAA for healthcare. Enforce RBAC at the database level — don't rely on the LLM to refuse. Data clean rooms or query approval workflows for highly sensitive datasets.

Sources

  1. dbt — Semantic Layer — accessed 2026-04-20
  2. DPDPA 2023 — accessed 2026-04-20

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