AI SQL Developer Training
Level
basicDuration
24h / 3 daysDate
Individually arrangedPrice
Individually arrangedAI SQL Developer Training
The “AI SQL Developer” training is a practical course showing how to use AI in daily work with Microsoft SQL Server without relinquishing control over your code. In this training you will learn how to use AI as an assistant, consultant, code reviewer, or pair programmer but without giving it architectural decisions
Requirements
- Access to ChatGPT, preferably at least the “GO” version
- Knowledge of relational database concepts and basics of SQL / T-SQL
- Familiarity with the Windows environment
What You Will Learn
- Implementation, analysis, and refactoring of SQL / T-SQL queries with AI assistance, including detecting syntax, logic and semantic errors
- Debugging code, writing procedures, functions, views, and analyzing side effects of queries
- Using AI for code review, refactoring, and simplifying existing code — eliminating unnecessary JOINs and repeated conditions
- Writing queries faster while maintaining full control and making conscious decisions about readability vs performance
- Translating complicated SQL into business language and recognizing incorrect or unsafe AI suggestions
- Conscious and safe use of AI in working with databases
Who is this training for?
People working with Microsoft SQL Server-
People who know basic SQL / T-SQL
-
Programmers who want to increase productivity when working with SQL
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Developers focused on query performance and architecture who want to significantly increase their work effectiveness
Training Program
AI as a database developer tool
- What AI is in the context of working with SQL / T-SQL
- What AI is not in this context
- AI as assistant, consultant, and code reviewer
- Typical AI applications in SQL / T-SQL work
- AI limitations — key aspects
- When AI should not be trusted
- Module summary
Preparing the work environment
- Creating a database for training
- Preparing tables and data
AI as part of the SQL developer workflow
- AI in SQL Server Management Studio (SSMS)
- Working with various SQL artifacts
- Safety in AI usage — a critical aspect
- Building context for queries for AI
- AI-assisted workflow model
- Module checklist as training material
Practical introduction to AI – ChatGPT
- What AI (Artificial Intelligence) is
- How AI works
- Pattern of a correct prompt
Finding and analyzing SQL / T-SQL errors
- Why SQL debugging is challenging
- Types of SQL and T-SQL errors
- Syntax errors
- Logic errors
- Semantic errors
Writing SQL and T-SQL queries with AI assistance
- Why AI is good for writing SQL
- AI as pair programmer model
- Generating SELECT queries with AI
- Generating JOIN with AI
- GROUP BY and HAVING with AI support
- CTE (Common Table Expressions)
- Subqueries — generation and refactoring
- Writing stored procedures with AI
- Writing functions with AI
- Views — generation and organization
- AI as first-draft query generator
- Validating AI-generated queries
- Checklists for this module
SQL / T-SQL code refactoring
- What code refactoring is
- What SQL / T-SQL refactoring is
- Simplifying SQL / T-SQL queries
- Eliminating unnecessary JOINs
- Eliminating repeated conditions
- Changing subqueries to CTE
- Changing CTE to JOIN
- Standardizing T-SQL style
- Readability vs performance — architectural decisions
- Refactoring model with AI
- Module checklists
Translation and analysis of queries
- Introduction to code review
- Why reading SQL / T-SQL is harder than writing it
- Analyzing business logic of queries
- Analyzing side effects of queries
- Translating SQL into natural language
- Translating natural language into SQL
- Working with someone else’s code — why it’s difficult
- Ideal applications of this module
- Analysis with AI model
