Querying a Table
Explore how to use SQL queries with AI assistance to retrieve and narrow down data from tables. Understand the importance of selecting specific columns and practice editing queries to gain confidence in querying data effectively.
Step 1: Start with a question
Let’s imagine you’re exploring a table named employees. You don’t know what’s in it yet.
Ask yourself:
What data do I actually want to see first?
Maybe something simple like:
Show me all the employee records.
Now, don’t write SQL yet; ask AI to translate that question.
Prompt: Write a SQL query that displays all employees from the employees table.
Step 2: Run and observe
AI will probably return:
You should see something like:
ID | First Name | Last Name | Department | Salary |
1 | Alice | Kim | Marketing | 75,000 |
2 | Raj | Patel | Engineering | 95,000 |
3 | Maria | Lopez | Finance | 88,000 |
4 | David | Nguyen | Engineering | 102,000 |
5 | Sofia | Rossi | Design | 72,000 |
6 | James | Olsen | Sales | 68,000 |
7 | Lina | Chen | HR | 64,000 |
8 | Omar | Hassan | Operations | 85,000 |
9 | Ethan | Brown | Support | 58,000 |
10 | Isabella | Garcia | Engineering | 97,000 |
Take a moment to visualize your data structure:
Each row represents a single record, in this case, an employee.
Each column represents a specific piece of information about that record.
When you write:
SELECT *
You’re telling SQL: Show me every column in the table.
Step 3: Narrow the focus
Seeing everything is messy. Let’s make our question sharper: Show me each employee’s first name and department.
Use the AI again.
Prompt: Write a SQL query that shows only the employee’s first name and department from the employees table.
AI might give you:
Run it. You’ll now see a cleaner table with fewer columns and the same rows.
Ask yourself:
Which question gives you a clearer picture?
Why might analysts rarely use
*in real life?
Step 4: Reflect and interpret
Let’s think together:
SELECT: Which columns do I want?FROM: Which table has them?*: All columns (not always useful).
A good SQL query isn’t just syntax; it’s a clear question. Every line you write should match the question you’re asking.
Step 5: Your turn to tweak
Now you’ll make a small edit yourself. No AI this time, type directly in the SQL widget.
Challenge: Change the query so it shows
first_name,last_name, andsalaryof allemployees.
Did you get a new table with just those three columns?
That’s your first hand-written SQL query. Small tweak, big step, you’ve moved from AI-generated to AI-assisted.
Quick recap
SQL Keyword | What It Means | Example |
| Pick which columns to show |
|
| Tell SQL which table to use |
|
| Shortcut for “all columns” |
|
Practice prompt ideas
Use the AI Prompt Widget to try your own:
Show me the names and salaries of employees.
Show only the employee id and department.
List all columns again using SELECT *.
Then tweak one of them yourself to add or remove a column.
Reflect before moving on
Ask yourself:
How did my question in plain English become SQL?
Why is narrowing columns helpful?
What would I ask next to start learning about the types of data this table holds?
You’ve just completed Lesson 1. You can now ask AI a data question in natural language, read and run the SQL it produces, and manually edit it to explore new columns.