Training course · Vibe Coding · Python Development

Vibe Coding for Python Developers

Build software in natural language: from prompt to app, rapid iteration, quality and security checks, and turning prototypes into production.

Duration
2 days (14 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

This course connects Vibe Coding with the everyday processes of Python Development. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

Vibe coding is a new style of software development: you describe in natural language what an application should do, and AI tools generate the code for it. Instead of writing every line yourself, you steer through descriptions, examples and feedback, test the result and refine it in short loops. This dramatically speeds up prototypes, internal tools and experiments. This vendor-neutral course shows you how to use vibe coding productively with tools such as Claude Code, Cursor, Copilot or browser-based app builders.

Knowing the limits matters just as much as speed. Uncontrolled generated code can be unmaintainable, insecure or simply wrong. You will therefore learn to state requirements precisely, safeguard results with tests and reviews, spot typical security flaws and steer the architecture deliberately. Finally, you will map out a clear path from quick prototype to production-ready application, covering version control, deployment, documentation and a clean handover to the team that will run and maintain the solution over the long term.

Python powers web back ends, automation, data pipelines and AI applications – often inside the same organisation, which makes codebases notoriously diverse. AI coding assistants and agents have a particularly visible impact here: they write pytest suites around your existing fixtures, add type hints until mypy passes cleanly, migrate legacy scripts to modern packaging with pyproject.toml and walk you through an unfamiliar Django or FastAPI project in minutes rather than days. The key is steering these tools with the right context: project conventions, Ruff rules, test commands and architectural decisions belong in instruction files and prompts. Once you master that, you can delegate routine work with confidence and focus on design, review and business logic.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Python back-end developers working with Django, Flask or FastAPI
  • Automation and scripting specialists in IT and business units
  • Data and ML engineers maintaining production Python code
  • Tech leads responsible for code quality and review processes
  • Developers modernising legacy Python codebases

Typical challenges we address

  • Ageing codebases with no type hints and patchy test coverage
  • Dependency sprawl across requirements.txt, Poetry, uv and virtual environments
  • Time-consuming reviews and inconsistent style despite linters and formatters
  • Onboarding into unfamiliar projects that rely on undocumented tribal knowledge

What you will be able to do

  • You build working applications in natural language with a suitable AI tool.
  • You phrase requirements and constraints so that generated results meet your expectations.
  • You iterate purposefully, fix errors efficiently and stay in control of the project state.
  • You safeguard generated code with tests, reviews and security checks.
  • Have AI agents reliably deliver pytest suites, type hints and refactorings across your Python codebase.
  • Encode project conventions, tooling such as Ruff, mypy and uv, and test commands so the assistant follows them consistently.
  • Review generated Python code systematically for correctness, security and maintainability before it is merged.

Course outline

Day 1

Module 1 Vibe coding fundamentals
  • What vibe coding is and how it differs from traditional and agentic development
  • The tool landscape: AI editors, coding agents and browser-based app builders
  • Good use cases: prototypes, internal tools, experiments, learning projects
  • Roles: you as product owner, the AI as the one doing the typing
Module 2 From prompt to app
  • Describing requirements as a short spec covering users, features and boundaries
  • Choosing the tech stack and project structure deliberately rather than leaving it to the AI
  • Generating user interfaces from descriptions, sketches and reference examples
  • Adding data, APIs and persistence step by step
Module 3 Iterating and fixing problems
  • Working in small, verifiable steps and committing regularly
  • Feeding error messages, logs and screenshots back to the AI
  • Recognising stuck sessions, rolling back and restarting with fresh context
  • Creating project rules and context files to avoid repeating yourself
Module 4 Securing quality and safety
  • Having the AI generate tests and critically assessing what they actually prove
  • Reviewing generated changes for readability, duplication and needless complexity
  • Spotting typical security flaws: secrets in code, missing validation, exposed endpoints
  • Assessing dependencies, licences and data protection in generated solutions

Day 2

Module 5 From prototype to production
  • Setting criteria for evolving a prototype versus rebuilding it
  • Refactoring, cleaning up architecture and documenting with AI support
  • Setting up version control, CI and deployment for generated applications
  • Planning operations, monitoring and handover to a team or IT
Module 6 Practice lab: Python Development
  • Generating pytest tests
  • Type hints and mypy clean-up
  • Django migrations and ORM refactoring
  • FastAPI endpoints with Pydantic
  • Modernising legacy scripts
  • Understanding and documenting a codebase
Module 7 Data protection, quality & transfer to daily work
  • Define which source code may be sent to cloud-hosted models, keep secrets from .env files strictly out of context, and check generated code for licence carry-over and newly introduced PyPI dependencies (typosquatting, supply-chain risk). For products with digital elements, obligations under the Cyber Resilience Act are becoming increasingly relevant.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Generating pytest tests

The assistant inspects functions and existing fixtures, writes parametrised pytest tests covering edge cases and mocks for external services, then runs them iteratively until they pass and reach the coverage you are aiming for.

Type hints and mypy clean-up

Untyped code is annotated module by module with type hints, TypedDicts and Protocols. The agent runs mypy or Pyright, interprets the output and fixes issues step by step without changing runtime behaviour.

Django migrations and ORM refactoring

Model changes are planned together with data migrations, makemigrations conflicts are resolved and N+1 queries are fixed with select_related or prefetch_related. The AI flags risks to existing production data before you deploy.

FastAPI endpoints with Pydantic

From a functional description or an OpenAPI draft, the assistant produces Pydantic models, routers, dependency injection and validation logic, complete with error handling and matching tests using the TestClient.

Modernising legacy scripts

Old scripts become proper packages with pyproject.toml, global state is removed, argparse or Click interfaces are added and Ruff rules applied. The agent documents every change clearly in the commit history.

Understanding and documenting a codebase

The assistant explores an unfamiliar repository, produces architecture overviews, explains data flows between modules and fills in missing docstrings in Google or NumPy style so new team members become productive faster.

Possible hands-on projects

Log analysis CLI

A Typer-based command-line tool that reads application logs, clusters error patterns and outputs a concise summary as Markdown or JSON – shipped with tests and proper packaging.

FastAPI microservice with a database

A small REST service with SQLAlchemy, Alembic migrations, Pydantic validation and a Docker setup, planned, implemented and tested end to end by an AI agent.

Dependency audit script

A script that lists all project dependencies, checks for outdated or vulnerable packages against public advisory databases and produces a prioritised upgrade report.

Prerequisites

Basic programming knowledge and some Git experience are helpful; you should be able to read simple code and make sense of error messages. Solid Python skills plus hands-on experience with Git, virtual environments and at least one framework such as Django, Flask or FastAPI.

Technical requirements

Your own laptop with an up-to-date browser, Git and an AI coding tool of your choice, such as an AI editor or coding agent with an active account.

Data protection & compliance

Define which source code may be sent to cloud-hosted models, keep secrets from .env files strictly out of context, and check generated code for licence carry-over and newly introduced PyPI dependencies (typosquatting, supply-chain risk). For products with digital elements, obligations under the Cyber Resilience Act are becoming increasingly relevant.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Tue, 24 November 2026 – Wed, 25 November 2026 Online Request
  • Tue, 1 December 2026 – Wed, 2 December 2026 Munich Request
  • Wed, 9 December 2026 – Thu, 10 December 2026 Online Request
  • Thu, 17 December 2026 – Fri, 18 December 2026 Stuttgart Request
  • Wed, 13 January 2027 – Thu, 14 January 2027 Berlin Request
  • Tue, 19 January 2027 – Wed, 20 January 2027 Online Request

Frequently asked questions

Is vibe coding only suitable for quick prototypes?

Vibe coding shines with prototypes, internal tools and experiments. With the right practices, namely clear specifications, tests, reviews and sound version control, results can also be turned into production-ready applications. The course teaches you to manage that transition deliberately and to recognise early on when rebuilding is the wiser option.

Which tools are used in the course?

The course is vendor-neutral. We work with the main tool categories: AI editors such as Cursor or Windsurf, coding agents such as Claude Code or Codex, and browser-based app builders. You are welcome to use your preferred tool, as the principles of specification, iteration, quality and security apply to all of them.

How should I deal with security risks in generated code?

Generated code often contains typical weaknesses such as hard-coded credentials, missing input validation or overly open interfaces. You learn to check specifically for these patterns, build security requirements into the specification from the start, use automated scanning and require a deliberate human sign-off before every deployment.

Does AI-assisted coding work in large Django monoliths?

Yes, provided the agent gets the right context. On the course you learn to create instruction files describing architecture rules, app structure and test commands, and to scope tasks so the assistant works on individual apps or modules. Changes stay manageable and reviewable even when the overall project is very large.

Will AI replace my code reviews?

No. AI assistants speed up writing code and can prepare reviews by pointing out missing tests or risky sections, but functional and security accountability stays with your team. We show how to combine review checklists, static analysis and tests so that generated code meets exactly the same quality bar as hand-written code.

How much does “Vibe Coding for Python Developers” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 2 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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