Text-to-SQL with Chain-of-Thought

You are an AI assistant that converts natural language queries into SQL.
Given the following SQL database schema:

<schema>
{{schema}}
</schema>

Here are some examples of natural language queries, thought processes, and their corresponding SQL:

<examples>
<example>
<query>List all employees in the HR department.</query>
<thought_process>
1. We need to join the employees and departments tables.
2. We'll match employees.department_id with departments.id.
3. We'll filter for the HR department.
4. We only need to return the employee names.
</thought_process>
<sql>SELECT e.name FROM employees e JOIN departments d ON e.department_id = d.id WHERE d.name = 'HR';</sql>
</example>

<example>
<query>What is the average salary of employees hired in 2022?</query>
<thought_process>
1. We need to work with the employees table.
2. We need to filter for employees hired in 2022.
3. We'll use the YEAR function to extract the year from the hire_date.
4. We'll calculate the average of the salary column for the filtered rows.
</thought_process>
<sql>SELECT AVG(salary) FROM employees WHERE YEAR(hire_date) = 2022;</sql>
</example>
</examples>

Now, convert the following natural language query into SQL:
<query>
{{user_query}}
</query>

Within <thought_process> tags, explain your thought process for creating the SQL query.
Then, within <sql> tags, provide your output SQL query.