NL2SQL hype vs reality

I was getting pretty tired of hearing about how NL2SQL is gonna revolutionize our analytucs workflows, so I decided to actually test it properly.

I ended up comparing three different approaches:

1. Function templates - basically just pre-built SQL with parameters (boring but works)
2. OpenAI function calling - where the AI picks which function to use
3. OpenAI NL2SQL - the holy grail where you just ask questions in english

Honestly thought the AI stuff would be way better than it turned out to be. But maybe I'm just old school.

The data setup (boring but necessary)

So first I needed some decent test data that wasn't totally fake looking. I work in e-commerce so I just built a generator that makes:

• 1000 fake users (split them into the usual segments we use - New, Active, VIP, Churned)
• 500 products spread across different categories
• A bunch of orders that actually make sense

generator = EcommerceDataGenerator()
generator.generate_all_data()
print("\n🎉 Dataset generation complete!")

Nothing too fancy but it gives me something realistic to work with:

Users by segment:

Products by category:

Order statistics:

Pretty decent spread for testing.

Actually running the tests

Ok so I picked 4 queries that we actually use at work:

1. Top selling products (classic)
2. VIP segment stuff
3. Revenue trends over time
4. Category performance

print(" Running comprehensive analytics comparison...")
comparison = AnalyticsComparison()
results = comparison.run_comparison()

Ran each approach against all the queries and timed everything.

The results (function templates win, surprise surprise)

So I was expecting the AI stuff to at least be competitive, but wow:

Function templates just absolutely destroyed everything else. Like not even close.

Breaking it down by query

The NL2SQL thing was particularly frustrating - it kept trying to query columns that don't exist or getting confused about date formats. Failed on half the queries which is... not great for something that's supposed to be the future. I spent like an hour trying to figure out why it couldn't understand our simple date column.

Why NL2SQL actually breaks down (the real problem)

So after debugging this stuff for way too long, I figured out the fundamental issue. It's not that the AI is dumb - it's that business questions are way more ambiguous than we think.

Take something simple like "show me declining customers". Sounds straightforward, right? But the AI has to choose between:

• Declining order frequency vs declining order value?
• Over what time period? Last month? Quarter? Year?
• How much decline counts as "declining"? 10%? 25%?
• Do we include one-time buyers or just repeat customers?
• What about seasonal variations?

Each choice leads to completely different SQL. And honestly, the AI just picks one randomly. Half the time it's not what you actually meant.

Demo vs reality gap

The demos they show you are always super clean:

-- What they demo:
SELECT customer_id, last_order_date 
FROM customers 
WHERE last_order_date < '2024-01-01'

But in real life, you get these 50-line monsters that are impossible to debug:

-- What you actually get in production:
WITH customer_monthly_stats AS (
  SELECT 
    c.customer_id,
    c.email,
    DATE_TRUNC('month', o.order_date) as month,
    COUNT(o.order_id) as order_count,
    SUM(o.total_amount) as total_spent,
    LAG(order_count, 1) OVER (PARTITION BY customer_id ORDER BY month) as prev_month_orders,
    -- ... 40 more lines of increasingly complex logic
  FROM customers c
  LEFT JOIN orders o ON c.customer_id = o.customer_id
  -- More joins and subqueries...
),
churn_risk_calculation AS (
  -- Another 20 lines of nested calculations
)
SELECT lots_of_complex_stuff FROM churn_risk_calculation
WHERE some_complicated_business_logic;

And when this thing returns wrong results? Good luck figuring out where the error is buried in that mess.

Why function templates win (spoiler: speed matters)

So function calling is 77x slower and NL2SQL is 167x slower. That's... a lot.

Performance gets worse under load

I also did some quick load testing to see what happens when you actually have multiple people using this stuff. The results were... not good for the AI approaches:

Basically, as you add more load, the AI approaches completely fall apart while templates stay rock solid. This makes sense - templates are just running SQL, while the AI stuff has to make API calls and generate new queries every time.

What each approach is actually good for

Basically you're trading speed for "intelligence" but honestly I'm not sure the intelligence is worth it most of the time.

My take after running this

Honestly, what I actually think

Function calling is ok I guess - It picked the right functions every time which was actually pretty cool to see. Like it really did understand what I was asking for. But 2.6 seconds per query? That's gonna add up fast if you're running this stuff in production. Our dashboards would timeout.

NL2SQL is... complicated - Look, when it works it's pretty magic. Just ask a question and get an answer. But it failed half my tests because it doesn't really understand our schema that well. Plus it's slow as hell. I mean really slow. Like painfully slow.

Maybe just use all three? - I know this sounds like fence-sitting but honestly each one has its place. Templates for the stuff you run all the time, function calling when you want some AI help but need reliability, and NL2SQL for when you're just exploring and don't mind if it breaks. Which it will.

The hybrid approach that actually works

After thinking about this more, I realized the smart play is probably using all three strategically. Here's what I'm thinking:

class HybridAnalytics:
    def __init__(self, db_connection):
        self.templates = SQLTemplateAnalytics(db_connection)
        self.function_calling = OpenAIFunctionCallingAnalytics(db_connection)
        self.nl2sql = OpenAINL2SQLAnalytics(db_connection)
    
    def analyze(self, question: str):
        """Route questions intelligently based on use case"""
        
        # 80% of our questions are predictable patterns
        if self._is_standard_analytics(question):
            return self.templates.run_appropriate_function(question)
        
        # 15% benefit from AI parameter selection
        if self._needs_guided_analysis(question):
            return self.function_calling.execute_query_with_function_calling(question)
            
        # 5% are truly exploratory - use NL2SQL but with safety limits
        return self.nl2sql.safe_query(question, max_execution_time=30)

The idea is:

• Templates for your bread-and-butter dashboard queries (daily revenue, top products, etc.)
• Function calling when business users need something custom but structured
• NL2SQL only for true exploration where you don't mind if it breaks

Making NL2SQL safer

If you do use NL2SQL, at least put some guardrails on it:

def safe_nl2sql(question: str) -> pd.DataFrame:
    """NL2SQL with safety constraints"""
    
    sql = openai_generate_sql(question)
    
    # Validate before executing
    validate_query(sql, constraints={
        'max_joins': 3,                    # Prevent crazy complex queries
        'max_subqueries': 2,               # Keep it simple
        'forbidden_keywords': ['DELETE', 'UPDATE', 'DROP'],  # Read-only
        'require_limit': True,             # Always limit results
        'max_execution_time': 30           # Don't let it run forever
    })
    
    return execute_safely(sql)

This won't fix the fundamental problems, but at least it won't bring down your database.

Mistakes to avoid (learned the hard way)

Since I've been down this rabbit hole, here are some things I wish someone had told me:

Don't start with NL2SQL - I know it's tempting because it looks so cool in demos, but you'll spend months debugging weird edge cases. Start with templates for your core use cases, then add AI on top.

Don't ignore performance testing - Your NL2SQL queries might work fine with 100 rows in dev, but completely timeout with real data volumes. Always test with production-sized datasets.

Don't trust generated SQL blindly - Just because the query runs doesn't mean it's correct. I've seen AI generate technically valid SQL that was completely wrong for the business question. Always validate results against known good data.

Don't forget to document business logic - One advantage of templates is you can document exactly what your metrics mean. "Declining customers" isn't ambiguous when you write it as detect_churn_risk(lookback_days=90, decline_threshold=0.25).

Don't skip the boring stuff - Database indexes, query optimization, proper error handling. This stuff matters way more than which AI model you use.

Final thoughts

Look, I went into this thinking the AI stuff would be way more impressive than it was. Don't get me wrong - it's cool that you can just ask questions in english and get SQL back. But there are many combinations of nl2sql that I don't want to rely upon in production.

Maybe in a few years the AI stuff will get faster and more reliable. But for now, if you need speed and reliability, just write the SQL yourself and call it using a template or a function call.