SQL AI-Assisted
Use AI to write SQL faster — without giving up the ability to understand, debug, and verify the query yourself.
18 lessons · 3 course parts · 3 capstone projects · Built-in AI-assisted editor · Certificate included.
Why Learners Choose SQL AI Assisted
Turn AI into a SQL Copilot
Verify Before You Trust
Read AI-generated SQL line by line and catch hallucinated columns, incorrect joins, weak filters and misleading logic.
Work Faster on Real Analysis
Use AI-assisted workflows for aggregation, joins, filtering, exploration and business reporting — while keeping human control.
Build 3 Applied Capstones
Finish with an AI-assisted query build, an end-to-end business report, and an AI output audit.
Learn the SQL Skill That Matters When AI Can Generate the First Draft
AI can produce SQL in seconds. The valuable skill is knowing how to ask for the right query, inspect what AI produced, identify quiet errors, test the logic, and decide whether the output can be trusted. SQL AI Assisted teaches that complete workflow instead of turning learners into copy-and-paste users.
Why this course is different
You are not learning “how to ask ChatGPT for SQL.” You are learning a transferable workplace workflow: define the business question → give AI the right context → generate SQL → inspect the logic → debug errors → verify the result → explain the finding → apply human judgment.
Skills you'll gain
- Write stronger prompts using schema, relationships, SQL dialect and expected output
- Read AI-generated queries instead of accepting them as a black box
- Debug syntax, JOIN, filtering and aggregation mistakes
- Use AI to accelerate business metrics, joins and exploration
- Explain results clearly to non-technical stakeholders
- Detect hallucinated tables, columns and assumptions
- Apply privacy, security and human-oversight judgment
What you actually practise
- AI-assisted SQL generation inside the course workflow
- Weak prompt → stronger context-rich prompt
- Query verification before trusting the output
- Debugging realistic AI mistakes
- End-to-end analytical reporting with AI assistance
- Auditing generated SQL for technical and responsible-use risks
- Exactly three capstone projects — one after each part
Who should take this course
- Analysts and business professionals who already know SQL and want to work more efficiently with AI
- Learners who completed SQL Advance and want the next step
- People already using ChatGPT, Copilot or another AI assistant for SQL who want a more reliable method
- Professionals who need to review AI-generated SQL rather than blindly trust it
Prerequisites
- SQL Advance, or equivalent comfort with joins, subqueries and aggregation
- No previous AI-tool experience is required
- No programming language beyond SQL is required
SQL AI Assisted Curriculum: 18 Lessons Across 3 Parts
The progression moves from directing and understanding AI-generated SQL, to applying AI across real analytical workflows, to judging when AI output should be corrected, rejected or escalated to a human.
Build the mental model for working with AI on SQL tasks — how to prompt it, read its output, debug it and use it for exploration without treating it as automatically correct.
- Introduction to AI for SQL
- Prompting AI for SQL
- Understanding AI-Generated SQL
- AI for Writing SQL Queries
- AI for Debugging SQL
- AI for Data Exploration
- Capstone 1: AI-Assisted Query Build
Apply AI-assisted SQL to business metrics, aggregation, multi-table joins, advanced filters, analytical problem solving and clear communication of results.
- AI for Aggregation & Business Metrics
- AI for Joins and Multi-Table Analysis
- AI for Filtering and Advanced Logic
- AI for Business Problem Solving
- AI for Explaining Results
- AI-Assisted SQL Workflow (End-to-End)
- Capstone 2: End-to-End Business Report
Learn where AI-generated SQL can fail and how to apply privacy, legal, ethical and professional judgment before generated output is used in real work.
- Hallucinations in AI
- Data Privacy & Security
- Legal Considerations
- Ethical Use of AI in Analytics
- When NOT to Use AI
- Human Oversight and Accountability
- Capstone 3: AI Output Audit
3 Hands-On Capstone Projects That Prove You Can Use AI Responsibly
Each project produces a different kind of evidence: you can build with AI, analyse with AI, and audit AI. The course contains exactly three capstones — one at the end of each part.
AI-Assisted Query Build
Start with a real business question, prompt AI to draft the SQL, then inspect, debug and verify the query yourself before accepting the result.
- Prompting AI for SQL
- Reading AI-generated SQL
- Debugging and verification
End-to-End Business Report
Use AI to help build an aggregation-and-join report that answers a business question, validate the SQL, and explain the final results in language a non-technical audience can use.
- AI for aggregation & joins
- Advanced filtering logic
- Business interpretation
AI Output Audit
Review AI-generated queries for hallucinated fields, logic errors, privacy concerns and inappropriate assumptions, then decide which outputs to trust, fix or reject.
- Hallucination detection
- Data privacy judgment
- Human oversight
See the AI + SQL Workflow You Will Practise
A good AI-assisted analyst does not type a vague request and paste whatever appears. The course trains a repeatable prompting and verification pattern.
Weak prompt
AI may generate
The verification questions students learn to ask
- Do these columns actually exist in the schema?
- Does AND match the business requirement?
- Are the stored values exactly what the query assumes?
- Did AI add assumptions that were never requested?
- Does the result make sense against the underlying data?
That verification step is the value. AI can draft SQL. The course teaches learners how to remain accountable for what the SQL actually does.
SQL AI Assisted Course Details
Frequently Asked Questions
Yes. SQL Advance or equivalent comfort with joins, subqueries and aggregation is recommended because this course focuses on using AI on top of an existing SQL foundation.
The course teaches learners to prompt AI for SQL, understand and debug generated queries, use AI for aggregation, joins, filtering and business analysis, explain results, detect hallucinations, protect data privacy and apply human oversight.
There are exactly three capstone projects: AI-Assisted Query Build, End-to-End Business Report and AI Output Audit.
Yes. The course includes an embedded AI tutor designed to provide course-specific explanations and guided support.
The course contains 18 lessons across three parts and is designed to be completed in about two to four weeks at a few hours per week.
Yes. Learners who complete the course receive a certificate of completion from the Canadian Institute of Artificial Intelligence.
No. The course teaches a transferable AI-assisted SQL workflow: provide context, generate SQL, read the query, debug it, verify the result, explain the output and apply human judgment.
No. The course is deliberately built around inspecting, correcting and verifying generated SQL rather than treating AI output as automatically correct.
Use the official DataSoSi FAQ page or Contact Us for current policy details.
Build the SQL Foundation Before You Add AI
SQL AI Assisted works best when learners already understand SQL well enough to challenge AI output rather than simply accept it.
Meet the Creator of the Course
Learn from an educator who works across data science, machine learning, AI, analytics, and applied business problems.
Dr. Mark Lokanan
Professor · Senior Machine Learning Engineer · Data Scientist · AI Specialist
Dr. Mark Lokanan is a professor at Royal Roads University and a Senior Machine Learning Engineer at Vedia. His work spans data science, machine learning, artificial intelligence, analytics, financial crime, and applied research. Drawing on both academic and industry experience, he designed the SQL AI Assisted course to teach learners how to combine SQL knowledge with AI judgment — using AI to accelerate query development while still understanding, debugging, verifying, and taking responsibility for the final analytical output.
