Let the Machines Argue: AI Query Validation in Snowflake (Part 2)

If Part 1 was about building trust, Part 2 is about giving your AI coworkers permission to bicker — productively.

Because if “trust, but verify” was the Cold War version of collaboration, “let the machines argue” is the modern data equivalent. You don’t want a single all-knowing LLM nodding along; you want a tiny debate club in your warehouse, fighting over the best SQL logic so you don’t have to.

Step 1: Draft the Query, Then Question It

Let’s say you generate a query with Cortex or Copilot to pull total revenue per customer, filtering for active accounts. Before you celebrate, toss that SQL back to another model and say:

-- Original AI-generated query
SELECT CUSTOMER_ID, SUM(REVENUE) AS TOTAL_REVENUE
FROM SALES
WHERE STATUS = 'ACTIVE'
GROUP BY CUSTOMER_ID;

Now, validate it with a second prompt:

SELECT CORTEX_AI('gpt-4o', 
  'Review this SQL for potential logic or filtering issues. 
   Suggest corrections if needed:',
  SQL_TEXT => $$SELECT CUSTOMER_ID, SUM(REVENUE) AS TOTAL_REVENUE
               FROM SALES
               WHERE STATUS = "ACTIVE"
               GROUP BY CUSTOMER_ID;$$);

If the model flags that STATUS might be a string mismatch or missing inactive customers that still have revenue, congrats — you’ve just built a code review system that works while you’re making espresso.

Step 2: The “Dual Model” Pattern

Here’s where it gets fun.

Don’t rely on a single AI engine. Use two — one to generate, one to critique. Like Lennon and McCartney, or if you prefer a more chaotic example, Axl and Slash.

The point isn’t that one model is “right.”
The point is that their disagreement is the value.

This approach often surfaces subtle bugs like:

Wrong aggregation level (monthly vs. yearly)
Unintended joins on surrogate keys
Filters missing for deleted rows

In other words: you’ve turned Snowflake into a garage band of AI reviewers, each one trying to play louder (and more accurate) than the last.

⚙️ Step 3: Make It Repeatable

Once you find a validation pattern that works, automate it.

Add a stored procedure or Snowflake Streamlit dashboard that runs your query through an AI check whenever someone commits new logic.

Something like:

CREATE OR REPLACE PROCEDURE VALIDATE_SQL (sql_text STRING)
RETURNS VARIANT
LANGUAGE SQL
AS
$$
  SELECT CORTEX_AI('gpt-4o', 
    'Validate this SQL for logic, performance, and governance compliance. 
     Return a risk score from 0 to 100 and a summary of issues.',
    SQL_TEXT => sql_text);
$$;

Then call it in your dev workflow or CI/CD pipeline:

CALL VALIDATE_SQL($$SELECT * FROM CUSTOMERS WHERE ID = 123;$$);

You’ll get back a JSON object saying something to the effect of:

{
  "risk_score": 12,
  "issues": ["No limit applied", "Unindexed column used in WHERE clause"]
}

Suddenly, data review meetings get a lot shorter — because the machines already had the argument for you.

Real-World Use Case: Detecting Logic Drift in Finance Reports

Let’s get practical.

Imagine your monthly revenue query has been quietly drifting — not because someone broke it, but because someone “improved” it.

Last quarter:

SELECT SUM(AMOUNT) 
FROM TRANSACTIONS 
WHERE STATUS = 'SETTLED';

Now:

SELECT SUM(AMOUNT) 
FROM TRANSACTIONS 
WHERE STATUS IN ('SETTLED', 'PENDING');

That’s a $40 million difference in “innovation.”

You can use Cortex to detect and explain this kind of logic drift automatically:

SELECT CORTEX_AI('gpt-4o',
  'Compare these two SQL queries and explain any differences 
   in filters, joins, or aggregation logic that might change the results. 
   Rate the impact as LOW, MEDIUM, or HIGH.',
  SQL_TEXT => $$-- Old Query
SELECT SUM(AMOUNT) 
FROM TRANSACTIONS 
WHERE STATUS = 'SETTLED';

-- New Query
SELECT SUM(AMOUNT) 
FROM TRANSACTIONS 
WHERE STATUS IN ('SETTLED', 'PENDING');$$);

Cortex will respond with something like:

{
  "differences": [
    "The new query includes PENDING transactions, expanding scope.",
    "This may double-count incomplete transactions."
  ],
  "impact": "HIGH"
}

Now imagine embedding that inside your Snowflake governance workflow — every time a developer updates a query in your analytics repo, AI checks whether it changes business meaning.

That’s not just validation.
That’s a governance amplifier.

🤘 Rock ‘n’ Roll Governance

Remember, the goal isn’t to replace human intuition — it’s to augment your paranoia.

AI is great at spotting what you miss, not what you mean.

So build your data process like a band:

One model writes riffs (queries)
Another critiques the solo (validation)
And you, the lead architect, decide what makes the final cut.

Because in the end, even AI needs a producer.

Wrapping It Up: Controlled Chaos, Verified

AI query validation isn’t about surrendering control — it’s about letting your copilots debate the finer points of logic while you hold the mic.

Let them argue about joins, aggregations, or semantics — you just make sure the amps don’t blow.

Because “trust, but verify” isn’t paranoia anymore.
It’s best practice with distortion turned up to eleven.

So crank it. Let your AI copilots scream into the void until the truth drops out.
You might even find that the loudest one is right.

🎶 Cue the validation anthem: