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Python Programming with LLM and Generative AI

About

  • Level: Beginner to Intermediate
  • Lectures: 40 hours
  • Self-study: 20 hours
  • Exercises: 323
  • Lines of Code to write: 1098
  • Format: e-learning + weekly online teleconference with instructor
  • Language: English or Polish

Description

This hands-on training course teaches practical workflows for using large language models and generative AI to accelerate Python development. The syllabus covers selected AI technologies (text and image generation), tool and account setup, prompt engineering, and integrating AI into real workflows. Hands-on labs focus on chat-based assistance, code generation and refactoring, automated test and documentation generation, code review techniques to detect hallucinations, and Python foundations needed for safe, testable AI-assisted coding.

Advantages

Participants will prototype features faster by leveraging LLMs for scaffolding, refactoring, and test generation, which reduces repetitive work and boilerplate. The course emphasizes evaluation and risk mitigation so learners can limit hallucinations and licensing risks, improve code quality and test coverage, and responsibly integrate AI tools into CI/CD pipelines and team workflows for more maintainable, observable software.

Target Audience

  • Software developers and engineers who write Python and want to speed up development with AI-assisted coding and testing.
  • Data scientists and ML engineers who want to combine model-driven analysis with reproducible software engineering practices.
  • QA engineers and test authors who want to automate test generation and improve coverage for AI-influenced codebases.
  • DevOps and CI engineers who integrate AI-assisted workflows into pipelines and production deployment processes.
  • Technical leads and architects who define maintainability, security, and licensing standards for AI-enabled development.
  • Advanced students and learners aiming for hands-on, production-focused skills in AI-assisted Python development.

Format

The course is delivered as a blended learning experience, comprising numerous short videos that progressively introduce concepts and techniques through a series of practical examples. The course format combines e-learning modules with weekly online teleconferences with the instructor for Q&A, discussions, and code reviews.

During the self-study phase, students complete practical exercises that apply the learned techniques. Each exercise is designed to have 100% test coverage, allowing students to verify their solutions. Additionally, students will have access to a spreadsheet to track their progress.

Students will also receive downloadable resources, including code samples, exercise templates, and reference materials to support their learning journey. Since 2015, we have refined our materials based on student feedback to ensure clarity, engagement, and practical relevance. All code listings undergo automatic testing (over 28,000 tests) to ensure accuracy and reliability. All materials, code listings, exercises, and assignments are handcrafted by our trainers without the use of AI. All case studies and examples are based on real-world scenarios drawn from our extensive experience in software engineering.

Working language of the course is either English or Polish.

Course Outline

  1. Overview of selected AI technologies supporting programming:

    • Text generation (including ChatGPT by OpenAI)
    • Image generation (including DALL·E by OpenAI)
    • Music and podcast generation

  2. AI technologies supporting programming:

    • Comparison of Github Copilot, AI Assistant, Junie and others
    • Plugin installation and account activation
    • Account creation
    • API key generation
    • Request limits
    • Access levels
    • License costs
    • Ethics of using AI

  3. Environment setup and introduction to programming:

    • Introduction to the Python language
    • Introduction to an Integrated Development Environment (IDE)
    • Programming language syntax elements: variables, comments, operators
    • Introduction to the Python console (REPL), syntax, indentation, and standards

  4. Working with an AI assistant:

    • Chat
    • Code explanation
    • Code generation
    • Refactoring
    • Test generation
    • Documentation generation
    • Commit message generation

  5. Code review techniques and pair programming with AI:

    • Most common mistakes
    • Hallucinations (what to trust and how not to be misled)
    • Formulating effective queries (prompt engineering)

  6. Python:

    • Data structures: int, float, bool, None, str, tuple, list, set, dict, list[tuple], list[list], list[dict]
    • Selecting, slicing, and unpacking elements
    • Conditional and control statements: if, else, elif, and, or, not, for, while
    • Files, paths, open modes, operations: reading, writing, appending
    • Functions: definition, return values, parameters, arguments
    • Exceptions: raising, catching, hierarchy, call stack
    • Object-oriented programming: classes, instances, attributes, methods, inheritance
    • Modules: standard library, installing new modules, import

  7. Group discussion on Artificial Intelligence:

    • Ethical challenges related to AI implementation
    • Legal aspects of using AI-generated code
    • Overview of AI use cases
    • The future of artificial intelligence
    • When we will achieve Singularity (AGI)
    • The future of AI-assisted developer tools
    • Future programming languages
    • Artificial intelligence and quantum computers
    • Career development prospects in the field of AI
    • Discussion on “Will AI replace the programmer profession?”

Our Experience

AATC trainers have been teaching software engineering since 2015. We have already delivered over 11,000 (eleven thousand) hours of software engineering training to more than 32,000 (thirty-two thousand) students worldwide.

Requirements

  • Basic knowledge of Python programming
  • Familiarity with using an IDE (e.g., PyCharm, VSCode)
  • Familiarity with using version control systems (e.g., Git)
  • Basic understanding of AI-assisted coding tools (e.g., GitHub Copilot, ChatGPT)

Setup

  • Newest version of Python
  • IDE of your choice (e.g., PyCharm, VSCode)
  • Git installed and configured
  • GitHub account
  • Web browser (e.g., Chrome, Firefox, Safari, etc.)

Apply

If you are interested in taking this course, please contact us at info@astronaut.center