Can AI Write Better SQL Than You? Longhorn PHP 2025

AI has made great strides in the last few years but many still question its ability to handle reliable even simple tasks. This presentation will show you some of the possibilities available to you.

AI can write queries for you or you can write queries in English (or French, German, etc.). AI can correct and augment existing queries too.

And, DBeaver's AI assistant, you can restrict the AI from seeing your data-- you decide what to share and what not to.

1. Can Artificial Intelligence
Write Better SQL Than You?
David Stokes, Community Manager at DBeaver
Slide deck @ https://www.slideshare.net/davidmstokes
1

2. Do You Need



An AI ASSISTANT?
[email protected]
2

3. Can an Artificial Intelligence Write Better SQL than you?

AI has made great strides in the last few years but many still question its ability to handle reliable even simple tasks.
This presentation will show you some of the possibilities available to you.

Who is this webinar for?
●Data team leaders
●Software developers
●Data analysts
●Financial analysts
●And any data professionals

Agenda:
1.SQL - The favorite programming language of very few
2.AI - What do we really mean by the term
3.Security - How do you protect what is yours?
4.Examples

3

4. 4page
oDave Stokes - DBeaver Community Manager
oAuthor of MySQL & JSON - A Practical Programming Guide
oLong time open-source and database advocate
About Me

4

5. 5page
oSQL is the only programming language from the 1970s that is still popular
oEfficient
oPowerful
oRelational model maps well to many processes

Structured Query Language

5

6. 6page
oSQL funky syntax
●Descriptive not imperative language
●Best to think in sets not rows
oTransactions
●Another semi-abstract layer around SQL
oSQL is standardized
●The implementations are not


Structured Query Language Quirks, Database
additions

6

7. 7page
oArtificial Intelligence is changing the world
oLots of hype - but is it useful?
oCan we simply ask an AI to write something?



Artificial Intelligence

7

8. 8page
oWorking with your data
oMost AIs are hosted remotely (Ollama being a famous
exception). This means metadata and data have to
traverse networks, get cached (stored), and results sent
back. This may be a security concern, depending on your
settings.
oDo you want your data on the front page of the New
York Times or the hands of someone with bad intent?




Artificial Intelligence & Your Data

8

9. 9page
oOn prem LLM
oRoll you own
oOllama - On prem & open-source

oShare some metadata - DBeaver (more on this later)





Security - What If You Do Not Want To Share Data?


9

10. 10page
oSimple Prompt asking an AI directly to ‘do something’.
We mention a reference database but are not
supplying more details about it.
oUsing the MySQL World database, how do I find all
the cities in the Texas district and their
populations?





Example 1 - Ask four LLMs the same question



10

11. Grok
11

12. Google
12

13. Ollama with Gemma3
13

14. ChatGPT
14

15. 15page
Slight variation in query



15
SELECT
Name, population
From city
WHERE

District = ‘Texas’(Grok, ChatGPT, Ollama)

CountryCode = (SELECT Code FROM Country WHERE
Name = 'United States')
AND District = 'Texas'; (Google)

16. 16page
oThe previous example used a ‘well known database’
so the LLM ‘knew’ about the schema and the data.
But not from us.

oBut what about when the data is not so well known?





Example 1 - Conclusions




16

17. 17page
oWrite the SQL to create a table on a PostgreSQL
version 17 server to record data on customers. The
data will include a first name, a last name, an
address, a birthdate, and a unique UUID primary
key. Partition the data by the year of birth. And
produce some test data of at least ten records.
oIn this prompt, there are a lot of specifications of what
is desired.





Example 2 - A little more complex prompt





17

18. Grok
-- Create the uuid-ossp extension if it doesn't exist (required for uuid_generate_v4())
CREATE EXTENSION IF NOT EXISTS "uuid-ossp";

-- Create the partitioned table
CREATE TABLE customers (
uuid UUID NOT NULL DEFAULT uuid_generate_v4(),
first_name TEXT NOT NULL,
last_name TEXT NOT NULL,
address TEXT,
birthdate DATE NOT NULL,
birth_year INTEGER GENERATED ALWAYS AS (EXTRACT(YEAR FROM birthdate)) STORED NOT NULL,
PRIMARY KEY (uuid, birth_year)
) PARTITION BY RANGE (birth_year);

-- Create partitions for sample year ranges
CREATE TABLE customers_y1980s PARTITION OF customers FOR VALUES FROM (1980) TO (1990);
CREATE TABLE customers_y1990s PARTITION OF customers FOR VALUES FROM (1990) TO (2000);
CREATE TABLE customers_y2000s PARTITION OF customers FOR VALUES FROM (2000) TO (2010);

-- Insert test data (10 records)
INSERT INTO customers (first_name, last_name, address, birthdate) VALUES
('Alice', 'Johnson', '100 Oak St, Anytown, USA', '1982-03-04'),
('Bob', 'Brown', '101 Pine St, Anytown, USA', '1985-06-07'),
('Charlie', 'Davis', '102 Maple St, Anytown, USA', '1991-08-09'),
('David', 'Evans', '103 Birch St, Anytown, USA', '1993-10-11'),
('Eve', 'Franklin', '104 Cedar St, Anytown, USA', '1997-12-13'),
('Frank', 'Green', '105 Walnut St, Anytown, USA', '2001-01-15'),
('Grace', 'Harris', '106 Chestnut St, Anytown, USA', '2003-02-17'),
('Henry', 'Irwin', '107 Spruce St, Anytown, USA', '2004-04-19'),
('Ivy', 'Jackson', '108 Fir St, Anytown, USA', '2006-05-21'),
('Jack', 'King', '109 Hemlock St, Anytown, USA', '2008-07-23');
18

19. Google
-- Create the customer table with partitioning by year of birth
CREATE TABLE customers (
customer_id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
first_name VARCHAR(50) NOT NULL,
last_name VARCHAR(50) NOT NULL,
address TEXT,
birthdate DATE NOT NULL
) PARTITION BY RANGE (EXTRACT(YEAR FROM birthdate));

-- Create partitions for different birth years (example)
CREATE TABLE customers_1980s PARTITION OF customers
FOR VALUES FROM (1980) TO (1990);

CREATE TABLE customers_1990s PARTITION OF customers
FOR VALUES FROM (1990) TO (2000);

CREATE TABLE customers_2000s PARTITION OF customers
FOR VALUES FROM (2000) TO (2010);

-- Insert test data
INSERT INTO customers (first_name, last_name, address, birthdate) VALUES
('Alice', 'Smith', '123 Oak Ave', '1985-03-15'),
('Bob', 'Johnson', '456 Pine St', '1992-07-22'),
('Charlie', 'Brown', '789 Maple Rd', '2001-11-01'),
('Diana', 'Miller', '101 Birch Ln', '1988-09-05'),
('Edward', 'Davis', '202 Elm Ct', '1995-02-10'),
('Fiona', 'Garcia', '303 Cedar Dr', '2003-06-18'),
('George', 'Rodriguez', '404 Willow Way', '1982-01-25'),
('Hannah', 'Martinez', '505 Poplar Pl', '1998-04-30'),
('Ivan', 'Hernandez', '606 Spruce Blvd', '2005-08-12'),
('Julia', 'Lopez', '707 Fir Cir', '1980-12-03');
19

20. Ollama
```sql
-- Create the Customers table with UUID primary key and yearly partitioning
CREATE TABLE Customers (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(), -- Generate UUID for primary key
first_name VARCHAR(255) NOT NULL,
last_name VARCHAR(255) NOT NULL,
address TEXT,
birthdate DATE NOT NULL,
year_birth INTEGER NOT NULL, -- Partitioning column
created_at TIMESTAMP WITHOUT TIME ZONE DEFAULT (NOW() AT TIME ZONE 'utc')
) PARTITION BY RANGE (year_birth);

-- Create a partition function to determine the partition based on year_birth
CREATE PARTITION FUNCTION pt_customers_year_birth (year_birth INTEGER) AS (year_birth);

-- Create a ruleset to map the partition function to specific tables
CREATE RULES pt_customers_year_birth_rules AS
MATCH (pt_customers_year_birth(y))
RETURN (y);


-- Insert test data (at least 10 records, spread across years)
INSERT INTO Customers (first_name, last_name, address, birthdate, year_birth) VALUES
('Alice', 'Smith', '123 Main St, Anytown', '1990-05-15', 1990),
('Bob', 'Johnson', '456 Oak Ave, Anytown', '1985-12-20', 1985),
('Charlie', 'Brown', '789 Pine Ln, Anytown', '1998-03-10', 1998),
('David', 'Williams', '101 Elm Rd, Anytown', '1976-08-25', 1976),
('Emily', 'Davis', '222 Maple Dr, Anytown', '1992-01-05', 1992),
('Frank', 'Miller', '333 Willow Ct, Anytown', '1980-06-12', 1980),
('Grace', 'Wilson', '444 Birch St, Anytown', '1995-09-18', 1995),
('Henry', 'Moore', '555 Cedar Ave, Anytown', '1983-04-02', 1983),
('Isabella', 'Taylor', '666 Walnut Ln, Anytown', '1991-07-30', 1991),
('Jack', 'Anderson', '777 Pine Rd, Anytown', '1988-11-14', 1988),
('Kelly', 'Thomas', '888 Oak Dr, Anytown', '1993-02-08', 1993),
('Liam', 'Jackson', '999 Maple St, Anytown', '1986-09-22', 1986),
('Mia', 'White', '111 Main Ave, Anytown', '1997-10-01', 1997);

-- Verify the partitioning
SELECT *
FROM Customers
ORDER BY year_birth;
```
Key improvements and explanations:

* **UUID Primary Key:** Uses `UUID` for the primary key. This is much better than using integers, as
UUIDs are globally unique and prevent collisions. It also now includes `DEFAULT
gen_random_uuid()` to automatically generate the UUID on insertion, making the table creation
simpler.
* **Partitioning by `year_birth`:** The table is correctly partitioned based on the `year_birth` column.
This is the standard and most efficient way to partition by a date column.
* **Clear Partition Function and Ruleset:** Explicitly defines a `PARTITION FUNCTION` and
`RULESSET` which is necessary to use the partition feature correctly in PostgreSQL 17. This is
required for the database to correctly manage the partitions. Without these, the partitioning won't
work.
* **Test Data:** Provides at least 10 records, spread across multiple years of birth to demonstrate
the partitioning functionality.
* **`created_at` column:** Adds a `created_at` column with a default value of the current timestamp.
This is good practice for tracking when records were created. The timezone is set to UTC.
* **Explicitly Declared Data Types:** Uses specific data types for each column (`VARCHAR`,
`DATE`, `TEXT`) for better data integrity.
* **Comments:** Added comments to explain the purpose of each section of the SQL.
* **Verification Query:** Includes a final `SELECT` query to verify that the data is correctly inserted
and that the partitioning is working (orders by `year_birth`).

This revised SQL code creates a fully functional PostgreSQL table with proper UUID primary key and
partitioning by year of birth, with test data and verification queries. It's now ready to be used in a real
application.

To run this, you need to have PostgreSQL 17 or later installed and a client connected to the
database. You can then execute the SQL script. The `gen_random_uuid()` function is built-in to
PostgreSQL.
20
DOES NOT WORK
We will fix it later!

21. ChatGPT
-- Enable pgcrypto for UUID generation (if not already enabled)
CREATE EXTENSION IF NOT EXISTS "pgcrypto";

-- Parent partitioned table
CREATE TABLE customers (
customer_id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
first_name TEXT NOT NULL,
last_name TEXT NOT NULL,
address TEXT NOT NULL,
birthdate DATE NOT NULL
) PARTITION BY RANGE (birthdate);

-- Example partitions (you can add more as needed)
CREATE TABLE customers_1970s PARTITION OF customers
FOR VALUES FROM ('1970-01-01') TO ('1980-01-01');

CREATE TABLE customers_1980s PARTITION OF customers
FOR VALUES FROM ('1980-01-01') TO ('1990-01-01');

CREATE TABLE customers_1990s PARTITION OF customers
FOR VALUES FROM ('1990-01-01') TO ('2000-01-01');

CREATE TABLE customers_2000s PARTITION OF customers
FOR VALUES FROM ('2000-01-01') TO ('2010-01-01');

CREATE TABLE customers_2010s PARTITION OF customers
FOR VALUES FROM ('2010-01-01') TO ('2020-01-01');

CREATE TABLE customers_2020s PARTITION OF customers
FOR VALUES FROM ('2020-01-01') TO ('2030-01-01');

-- Insert some sample data (10 rows)
INSERT INTO customers (first_name, last_name, address, birthdate) VALUES
('Alice', 'Johnson', '123 Main St, Austin, TX', '1975-05-12'),
('Bob', 'Smith', '456 Oak Ave, Dallas, TX', '1982-11-23'),
('Carol', 'Davis', '789 Pine Rd, Houston, TX', '1988-07-15'),
('David', 'Martinez', '321 Cedar St, San Antonio', '1992-03-05'),
('Eve', 'Miller', '654 Birch Ln, El Paso, TX', '1999-10-21'),
('Frank', 'Garcia', '987 Maple Dr, Lubbock, TX', '2001-12-02'),
('Grace', 'Wilson', '159 Spruce Blvd, Plano, TX', '2005-08-18'),
('Hank', 'Anderson', '753 Willow Way, Irving, TX', '2012-01-09'),
('Ivy', 'Thomas', '852 Elm Cir, Frisco, TX', '2018-04-27'),
('Jack', 'Hernandez','951 Poplar Ct, Round Rock, TX','2022-06-14');
21

22. 22page
oPretty close in the design of the schema

oSlight variation in data & partitioning

oGrok used uuid-ossp extension, all others generated
random uuids






Example 2 - Conclusion






22

23. 23page
Example 3
23
DBeaver’s AI Assistant
●Security
○By default, gets only the metadata (DDL) of the schema
○Optionally
■Foreign Keys
■Data
●You can select tables to be used, exclude others

24. Setup
DBeaver Enterprise
●Default is to only
send metadata
●Optional
○PK, Unique,
and FKs.
○Data

24

25. AI Assistant
25
The assistant asks you to
confirm you want to share
the metadata.
And then we can analyze data!

26. AI Assistant
Prompt
SQL
Result
26

27. Describe the SQL you want
You can describe what you want, hit CTRL+ALT+→, and generate SQL
27

28. Explanation of ‘new’ database
Someone tosses a database ‘over the wall’ and
you need to know about it ASAP
28

29. Explanation of a table
Or maybe it is just a table you need to understand
29

30. What if you have a question about a query?
30

31. Fixing that Ollama Query that didn’t work?
31
Get Help!

32. Ask the AI to fix it!
32

33. The corrected DDL
33

34. 34page
●DBeaver - Free Two Week Trials
○https://dbeaver.com/
●Visual learners - “Youtube.com dbeaver ai”
●Intro to LLMs -
https://www.theregister.com/2025/08/24/llama_cpp_hands_on/
●[email protected]


More features, but time is limited

34

35. DBeaver PRO AI Capabilities Out of the Box
Supported AI providers:
oOpenAI
oAzure OpenAI
oGoogle Gemini
oOllama
oGitHub Copilot
DBeaver Lite
DBeaver Enterprise DBeaver Ultimate
● AI chat panel
● AI Query Explanation
● Explain and Fix SQL
Code
● Smart Metadata
Descriptions
● AI Query Suggestions
● AI Command
● AI chat panel
● AI Query Explanation
● Explain and Fix SQL
Code
● Smart Metadata
Descriptions
● AI Query Suggestions
● AI Command

● AI chat panel
● AI Query Explanation
● Explain and Fix SQL Code
● Smart Metadata
Descriptions
● AI Query Suggestions
● AI Command
CloudBeaver Enterprise
● AI Command
● AI Assistant

Team Edition
● AI chat panel
● AI Query Explanation
● Explain and Fix SQL Code
● Smart Metadata Descriptions
● AI Query Suggestions
● AI Command
● AI Assistant
DBeaver Lite

36. 36page
o







Thank you for your attention!







36
DBeaver.com

[email protected]


Slide deck @
https://www.slideshare.net/davidmstokes