AI SQL Query Generator (Text-to-SQL RAG)

1. Problem Statement

Non-technical business stakeholders frequently require immediate structured database insights but lack deep SQL query translation knowledge, creating operational engineering backlogs.

2. Solution Overview

Designed an automated natural language pipeline that safely converts conversational prompts into complex SQL code. The platform validates query structures against database schemas before executing them safely.

3. Technical Architecture

The platform reads schema rules, passes structural parameters into LLM prompts, generates raw queries, test-executes them inside isolated database scopes, and handles exceptions cleanly.

4. Individual Contribution

• Developed dynamic data injection prompts that supply schema definitions without leaking private database records.
• Built an agentic auto-correction engine that automatically rewrites syntactically invalid SQL queries.
• Optimized SQL execution boundaries to secure data against relational database injections.

5. Technology Stack

6. Challenges Faced

Challenge: Complex analytical requests containing nested subqueries frequently caused query logic errors.
Resolution: Implemented few-shot chain-of-thought prompt templates to guide the model through multi-step relational reasoning tasks.

7. Key Learnings

Gained a deeper understanding of deterministic system guardrails, prompt security principles, and robust error handling when processing large tabular tracking data.

8. Impact & Results

Enabled non-technical end users to instantly extract analytical summaries, lowering technical query turnaround delays significantly.

9. Media Showcase