PROJECT 01
Personal prompt library
A tested set of prompt templates for your daily data tasks, organised and reusable.
TemplatesPatternsVariables
01 / OVERVIEW
What this course is about
Most people use AI tools like a search box and get average answers. In this course you learn why models behave the way they do, and the patterns that turn them into reliable helpers: clear structure, examples, step by step reasoning, structured outputs, chaining and evaluation. Everything is practised on real work like writing and reviewing SQL, debugging code and documenting data.
You learn by doing: every week has hands-on exercises on Google Classroom, and every module ends with something you built yourself.
01
Understand how LLMs behave
Tokens, context windows, temperature and why models make things up.
02
Write structured prompts
Role, context, task, constraints and output format.
03
Use proven patterns
Few-shot examples, step by step reasoning and self-checks.
04
Get structured output
Reliable JSON and tables you can use in code.
05
Evaluate prompts
Test sets and simple scoring instead of guessing.
06
Use AI safely at work
Prompt injection, data privacy and when not to trust the answer.
02 / CURRICULUM
Week by week
A clear plan from the first session to the last. The pace adapts to you: if you already know a topic we move faster, and if something needs more time we take it.
WEEK 1How LLMs work and prompt anatomy5 topics
- Tokens, context windows and temperature
- Choosing a model
- Hallucinations and limits
- Role, context, task and format
- Delimiters and XML tags
WEEK 2Core prompting patterns5 topics
- Zero-shot and few-shot
- Step by step reasoning
- Breaking tasks into steps
- Structured JSON output
- Self-critique and verification
WEEK 3Prompts for data work5 topics
- Generating and reviewing SQL
- Debugging Python and PySpark
- Documentation and data dictionaries
- Test data and regex
- Claude, ChatGPT and GitHub Copilot in practice
WEEK 4Advanced prompting and projects5 topics
- System prompts
- Prompt chaining
- RAG and tool use basics
- Evaluation sets
- Prompt injection and privacy
03 / PROJECTS
What you will build
Real projects you can put on GitHub and explain with confidence in interviews.
PROJECT 02
SQL assistant with evaluation
A prompt that writes and reviews SQL, measured against a test set of real questions.
Structured outputEvaluationSQL
PROJECT 03
Chained data workflow
A multi-step chain that profiles a CSV, suggests cleaning rules and writes a summary report.
ChainingJSONReports
04 / TOOLS
Tools and technologies
The same tools used by data teams in real companies.
ClaudeChatGPTGitHub CopilotClaude and OpenAI consolesPython (optional)Excel or Google Sheets
05 / IS IT FOR YOU
Who this course is for
Who it is for
- Data engineers, analysts and developers
- Students who want to learn faster with AI
- Professionals who use AI tools daily
- Anyone curious about building AI agents later
What you need
- No coding needed
- Access to an AI assistant such as Claude or ChatGPT (free plans work)
- Basic SQL or Python helps for some exercises
Roles it leads to
- Data Engineer with AI skills
- AI-assisted Analyst
- Prompt and AI Workflow Specialist
06 / HOW YOU LEARN
What you get
Live and one-to-one
Every session is just you and me, never a batch. No recordings, so we can talk openly about your code.
3 sessions a week
Each session is 1 hour 15 minutes, on weekdays, weekends or both. We agree the schedule before starting.
Google Classroom
Assignments, projects, quizzes, interview questions and homework, all organised in one place.
Real projects
You build projects like the ones companies run, and get feedback on your code and design.
Interview preparation
Common questions, live coding practice and mock interviews with honest feedback.
Job placement support
CV, LinkedIn, applications and interviews. I stay with you until you are hired, as long as you do the work.
07 / PRICING
Simple pricing
You pay for your time with me, not per course. All prices are in US dollars.
Estimated total for Prompt Engineering at the typical pace (4 weeks)
About 200 USD
Full refund if it is not right for you after the first session. Go faster and it costs less.About 200 USD
08 / YOUR MENTOR
Hikmat Ullah
Senior Data Engineer at Algo · Lahore, Pakistan
I build data platforms with Snowflake, Azure Databricks, Azure Data Factory and Microsoft Fabric every day, and I have been mentoring since 2021. I teach what I use at work, and I stay with you until you land the job.
7+years in data
100+students trained
80+placed in jobs
1:1always, never a batch
09 / FAQ
Questions about Prompt Engineering
Is prompt engineering still useful as models improve?+
Yes. Better models still need clear instructions, context and checks. The skill is less about tricks and more about communicating precisely and testing results.
Do I need to code?+
No. Coding helps for a few optional exercises, but the core of the course works in the chat apps.
How is this different from Generative AI for Data Engineers?+
This course is about using AI tools well. The Generative AI course is about building with LLMs in code: APIs, RAG, agents and pipelines.
How much does it cost?+
200 USD per month whichever course you choose. You can also pay 100 USD every 15 days or 50 USD per week. All prices are in US dollars wherever you live.
What is the refund policy?+
If the training is not right for you after the first session, you get a full refund. After that, payments already used for completed sessions are not refunded, but unused prepaid time can be refunded.
Is there a job guarantee?+
Yes. As long as you attend your sessions and complete the assignments and projects, I keep helping you with CV, LinkedIn, mock interviews and job applications until you are hired.
Can I join from another country or time zone?+
Yes. Students join from many countries. We pick session times that work for your time zone before we start.
10 / KEEP GOING
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View course →START WHEN YOU ARE READY
Start the Prompt Engineering course
Tell me your goals and your time zone. We plan your schedule together, and your first session can start this week.