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AI agent · SQL · Internal tool · 2025–present

AI SQL Troubleshooter

An AI tool that reviews thousands of lines of SQL and proposes fixes in minutes.

AI workflow owner & reviewer

Days to minutes
turnaround on a broken query
Human-gated
designed for broader-team verification before release

Sanitized implementation diagram

Diagram · implementation view

Explainable SQL review with a human release gate

A confidentiality-safe representation of the internal review path.

  1. Inspect

    Accept a large, unfamiliar SQL codebase for review. Surface probable errors and the statements they affect.

  2. Explain

    Return a correction with plain-language reasoning.

  3. Verify

    Keep a human in the loop before a fix is shipped.

Sanitized workflow diagram. Published content excludes employer SQL, data, screenshots, and identifiers.

My contribution
Workflow design, prompt behavior, review standard, and iteration
Hard constraint
Employer SQL stays confidential and outside portfolio content
What changed
Explained corrections reduced reliance on a few specialists

Built for a business-intelligence team, this AI-assisted tool reads large SQL codebases, flags likely errors, and writes corrections with plain-language explanations. It lowers the expertise a fix requires from expert to confident, so the work stops piling up on a few people.

Outcomes

  • Reviews thousands of lines of SQL and returns corrections in minutes.
  • Designed to reduce reliance on a few specialists by pairing proposed fixes with explanations and a human release gate.

Evidence & claim boundaries

Confidentiality
No employer SQL, data, screenshots, or identifiers are published; the workflow is described at a safe abstraction level.
Turnaround basis
The comparison reflects the team’s troubleshooting workflow before and after the tool. Its evidence basis is an internal observation with variable response times; public benchmarking and a response-time service guarantee are outside this claim.

Problem

Debugging long, unfamiliar SQL is slow, and it lands on a handful of people who know the codebase. Everyone else waits on them, so every broken query became a queue behind two or three experts. Scarce access to people who could safely change the code created the bottleneck.

Approach

I wanted to widen the door while preserving the review standard. The tool had to do more than spot a problem. It had to explain the fix clearly enough that a non-expert could apply it and trust the result.

So I guided AI to create a reviewer that reads thousands of lines of SQL, points to likely errors, and proposes concrete corrections with the reasoning attached. Clear reasoning makes each correction reviewable and supports a confident release.

Delivered scope

A reviewer that ingests a large SQL codebase, surfaces probable errors, and returns specific corrections with an explanation. I direct refinements against cases that arise in practice, keeping the output grounded in real review work.

Key decisions & trade-offs

I required explanation alongside detection. The value is in returning a correction a non-expert can read, understand, and apply safely. I reviewed the output against that standard.

Technology LLM platforms · SQL

Scope Workflow design · AI guidance · Human review