AI Can Generate SQL — But It Still Doesn’t Understand Your Data (or Your Business)

Everyone talks about text-to-SQL like it’s solved.

Just pass a prompt → get SQL → done.

But when you actually build it…

👉 Everything breaks.

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The Illusion

At first, it feels simple:

“Give schema + user query → generate SQL”

And yes, for simple queries:

SELECT COUNT(*) FROM users;

It works.

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Reality: Systems Are Not Simple

Real users ask:

“Show top customers last month by revenue excluding refunds”

Now the model must:

• understand joins
• filter refunds
• calculate revenue correctly

And this is where things fail.

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🧠 How Real System Actually Works

Instead of raw prompting, I built a pipeline.

User Query
   ↓
Context Builder (Schema + Docs)
   ↓
LLM (SQL Generation)
   ↓
Validation Layer
   ↓
Execution
   ↓
Response + Feedback

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1. Schema Injection (Foundation)

Before calling the LLM, I pass structured schema:

• tables
• columns
• relationships

Example:

Table: orders
Columns: id, user_id, price, status

Table: users
Columns: id, name

👉 This helps the model generate valid SQL

But:

👉 Large schema = confusion
👉 Small schema = missing context

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2. RAG (Retrieval-Augmented Context)

Schema alone is not enough.

So I added context retrieval (RAG):

• business definitions
• query examples
• known patterns

Example:

"Revenue = SUM(price) where status = 'paid'"

👉 This guides the model

But still:

👉 AI does not truly understand this 👉 It just uses it probabilistically

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⚠️ The Hidden Problem: More Context ≠ Better Results

At first, it feels logical:

“If we give more context, the AI will perform better.”

So we try:

• full database schema
• detailed documentation
• multiple examples

But something unexpected happens.

👉 The model starts hallucinating more.

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Why This Happens

LLMs don’t understand context like humans.

They:

• process tokens probabilistically
• try to match patterns
• get confused with too much input

When context grows:

• important signals get diluted
• irrelevant tables affect output
• model mixes unrelated concepts

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Real Example

You provide:

• 20 tables
• 200+ columns

User asks:

“Show top customers”

AI might:

• pick wrong table
• join unrelated tables
• use incorrect columns

👉 Not because it's bad 👉 But because too much context reduces clarity

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3. The AI Agent (Generation Layer)

The LLM acts like an agent.

Input:

• user query
• schema
• retrieved context

Output:

• SQL query

But problems still happen:

• hallucinated joins
• wrong aggregations
• incorrect filters

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4. Validation Layer (MOST IMPORTANT)

Before executing SQL, I validate:

• table existence
• column existence
• allowed operations
• query limits

👉 Without this → system breaks

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5. Feedback Loop (Continuous Improvement)

After execution:

• log bad queries
• track failures
• refine prompts
• improve retrieved context

👉 System improves over time

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❗ The Real Problem: Business Understanding

Even after all this:

AI still fails.

Because it doesn’t understand business meaning.

Example:

“Revenue”

In real systems:

• exclude refunds
• exclude failed payments
• include or exclude taxes

👉 AI doesn’t know this unless defined

And even then:

👉 It may still apply it incorrectly

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⚠️ Why AI Still Fails

Even with:

• schema injection
• RAG
• validation

AI still:

• guesses intent
• lacks reasoning
• produces logically wrong SQL

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🧠 Key Insight

AI is not replacing backend logic.

👉 It requires backend engineering around it

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🚧 Where I Am Right Now

I’m still improving this system every day.

• refining prompts
• improving schema selection
• improving RAG quality
• handling edge cases

👉 It’s not perfect

But it’s getting better.

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🏁 Final Thought

Don’t ask:

“Can AI generate SQL?”

Ask:

“Can I trust this in production?”

That’s the real challenge.