The Risks and Realities of LLM-Generated SQL Content (and How to Do It Right)

Introduction

As AI tools become more common in technical work, they’re starting to shape how SQL content is written and shared. That shift brings opportunities—but also some real risks if the content isn’t verified.

AI-generated SQL often looks correct on the surface, but underneath, it can include outdated syntax, mixed database commands, or performance-impacting practices that don’t belong in a production environment. This post breaks down what that means in practice, how to spot these issues, and how to use AI responsibly without putting your data at risk.

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What LLMs Are (and What They’re Not)

Large Language Models (LLMs) are AI systems trained on huge amounts of text data. They’re good at recognizing language patterns and generating content that sounds right.

What they can’t do is validate T-SQL against a SQL Server engine, keep up with version-specific changes, or guarantee that a recommendation is safe in your environment. They predict what’s likely to come next, not what’s technically accurate.

👉 Example:
Some AI outputs still recommend DBCC CHECKALLOC as part of database health checks. This command has been deprecated for years. The correct modern command is DBCC CHECKDB.

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Common Problems with LLM-Generated SQL

1. Outdated or Deprecated Features
   AI may surface commands no longer used or supported.
   Example: Recommending DBCC CHECKALLOC instead of DBCC CHECKDB.
2. Syntax from the Wrong Engine
   Mixing T-SQL with PostgreSQL or MySQL syntax is common.

-- Incorrect for SQL Server:
SELECT * FROM Sales LIMIT 10;

-- Correct for SQL Server:
SELECT TOP (10) * FROM Sales;

3. Risky “Best Practices”
It might suggest using NOLOCK to “fix blocking” without explaining dirty reads or integrity risks.
See Table Hints (Transact-SQL).

Security Exposure
Example: enabling xp_cmdshell without proper context or security controls. It’s disabled by default for good reason.

False Confidence
Because the content reads well, many assume it’s trustworthy. This is how bad practices spread quickly.

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When AI Can Actually Help

Used correctly, AI can be a useful assistant, not a source of truth. It works well for:

• Drafting documentation or code comments.
• Explaining concepts in simpler language.
• Generating starter queries for review.
• Summarizing official documentation.

The key is you doing the validation.

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Pros and Cons of Using LLMs for SQL

Pros	Cons
Fast draft content creation	High chance of inaccurate syntax
Summarizes docs quickly	May mix commands from other platforms
Useful for boilerplate code	Ignores performance and security context
Good for learning concepts	Can spread bad practices if unchecked

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How to Verify AI-Generated SQL

A practical workshop

Let’s walk through an example of how to properly review AI output.

Scenario

You ask an AI model for a query to find blocking sessions in SQL Server. It gives you:

SELECT * 
FROM sys.dm_exec_requests
WHERE blocking_session_id <> 0;

That’s not technically wrong, but it’s incomplete.

Step 1 — Check Microsoft Docs

Review sys.dm_exec_requests for column definitions, permissions, and behavior.

Step 2 — Expand the Query

Add more context to make it useful for troubleshooting:

SELECT
    r.session_id,
    r.blocking_session_id,
    r.status,
    r.command,
    DB_NAME(r.database_id) AS database_name,
    t.text AS query_text
FROM sys.dm_exec_requests AS r
CROSS APPLY sys.dm_exec_sql_text(r.sql_handle) AS t
WHERE r.blocking_session_id <> 0;

Step 3 — Test in a Safe Environment

Never run AI-generated scripts directly in production. Test in dev or sandbox first.

Step 4 — Document Your Final Version

Write your own explanation of the query and its purpose. Don’t just copy AI text. This builds trust in your content.

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Recommended Sources for Validation

• Microsoft Learn – SQL Server
• T-SQL Table Hints
• DBCC CHECKDB
• sys.dm_exec_requests
• SQL Server Release Notes
• Bob Ward | SQLskills | Itzik Ben-Gan

These should always be your first stop before you trust or publish anything.

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Real Example of a Bad Recommendation

A blog post recently included:

DBCC CHECKALLOC;

This is a deprecated command and shouldn’t be used. The correct modern equivalent is:

DBCC CHECKDB;

Microsoft Learn – DBCC CHECKDB provides the current, supported method for integrity checks.

This kind of small but critical mistake can lead to incomplete checks and undetected corruption.

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Summary

LLMs can make it easier to generate technical content, but they don’t replace actual expertise. When it comes to SQL Server, accuracy isn’t optional.

Use AI for speed — but never without validation.
Verify against Microsoft’s documentation, test everything in safe environments, and document it in your own words.

That’s how you keep your work credible and your systems safe.

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References

• Microsoft Learn – SQL Server Documentation
• T-SQL Table Hints
• DBCC CHECKDB
• sys.dm_exec_requests
• SQL Server Release Notes 2025
• SQLskills
• Bob Ward – Official Blog
• Itzik Ben-Gan

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✅ Final Thoughts
AI is a tool, not a shortcut to expertise. If you rely on it for SQL, make validation your first habit—not an afterthought.