Key takeaways:
- You build structured prompts
- You iterate and improve AI responses
- You critically evaluate the generated results
About
Artificial intelligence is rapidly changing the way we develop software. The question is no longer whether programmers will use AI, but who will know how to use it effectively.
This practical course teaches you how to use Google Gemini as a true AI pair programmer, integrated directly into your workflow: code generation, debugging, documentation, code review, and testing.
This is not a course about “copy-pasting AI-generated code.”
It is a course about how to think, build, and control AI through intelligent prompts.
Who is it for?
The course is entirely dedicated to programmers who want to professionally integrate AI into their workflow.
What benefits will participants gain?
- improved prompt quality
- reduced task completion time
- reduced testing time
- reduced time needed for documentation
What will the theoretical part cover?
By the end of the course, you will master techniques such as:
- Few-shot prompting
- Chain-of-thought reasoning
- Role prompting
- Prompt chaining
- Systematic prompt iteration
At the same time, you will understand AI limitations: hallucinations, biases, and ethical aspects.
What will the practical part cover?
After the course, you will be able to:
- generate well-structured code using AI
- reduce the number of bugs
- create automated documentation
- generate tests for code
- develop a mini end-to-end project using AI
Final exercise: Mini Integrator Project — where you will build a complete workflow using only Gemini and the techniques learned.
Why is this course worth it?
Because AI does not replace programmers. Programmers who know how to use AI will replace those who do not. This course gives you an AI working mindset that can be applied in any professional field.
Course Curriculum:
Introduction
- What is an LLM — an intuitive explanation, without mathematics
- How Gemini "reads" a prompt
- Anatomy of a prompt: Instruction · Context · Input · Output format
Basic Techniques
- Zero-shot / One-shot / Few-shot — when to use each
- Chain-of-Thought: "think step by step"
- Role prompting: "You are an expert in X"
Output Control
- Formats: JSON, Markdown, table, numbered list, prose
- Tone and style: formal, casual, technical, empathetic
- Length: words, paragraphs, tokens
- Negative prompting: "Do not use...", "Avoid..."
Advanced Techniques
- Prompt chaining: complex tasks → small steps
- Self-consistency: ask the same thing N times, compare results
- Tree of Thought: multiple directions in parallel
- Systematic iteration: how to improve a prompt methodically
Limitations, Ethics, and Best Practices
- Hallucinations: why they occur, how they look, how to reduce them
- Model biases — types and examples
- Prompt injection — basic awareness
- Golden rules: when NOT to use AI
Programmer Mindset with AI
- AI = pair programmer, not autopilot or replacement
- What Gemini knows about code and where it commonly makes mistakes
- Recommended workflows: TDD with AI, code review with AI
Code Generation
- Anatomy of a code prompt: language, version, context, constraints, examples
- The "stub + docstring" technique — you provide the skeleton, AI implements
- Specifying style: PEP8, type hints, error handling
Debugging and Code Review
- Debug prompt structure: code + error + what you tried
- Asking for explanations, not just quick fixes
- Code review by dimensions: security / performance / readability
Documentation and Tests
- Generating docstrings, README files, inline comments
- Unit tests: Given-When-Then structure
- Edge cases and test data generation
