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AI350: Vibe Coding for Developers NEW

Training: Artificial Intelligence

Participants learn in this hands-on developer workshop how to use LLMs as a coding copilot: higher speed without losing code quality, security, and maintainability. “Vibe Coding” means moving from idea to running code, iterating and refining with the model, and consistently adding tests/checks. Patterns include Spec/Plan First, Diff Requests, Constraint Prompts, Test-First, Review, and Hardening Prompts. In the practical exercise case, they build/extend a component end to end—from requirements to documentation, code review, and hardening—leaving with a personal toolkit they can apply in day-to-day work.

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Possible course languages: German

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Agenda:

Getting Started & Mindset
- What “Vibe Coding” is – and what it is not (speed vs. diligence)
- Strengths/weaknesses of LLMs in day-to-day development (reality instead of hype)
- Quality definition: “Done” means running, tested, reviewed, secure
Prompt Patterns Developers Actually Need
- Spec First: formulate requirements, acceptance criteria, edge cases cleanly
- Plan First: have the model propose architecture/steps first
- Constraints & Guardrails: lock down tech stack, style, performance, security, compatibility
- Diff/patch requests instead of “redo everything”: controlled changes to existing code
- “Explain then implement” vs. “Implement then explain”: when each pattern fits
From Zero to Feature: Scaffolding & Implementation with AI
- Generate structure (modules, classes, files, interfaces)
- API design & data models: fast proposals, then hard reviews
- Iteration loop: small increments, frequent compile/run/check cycles
Testing with AI
- Test strategy: unit/integration, “happy path” and edge cases
- AI as a test idea generator: better coverage, targeted negative tests
- Debug loop: provide error messages/logs with proper context, validate hypotheses
- Anti-pattern: tests that the model “prompts green” instead of verifying real quality
Refactoring, Code Review & Maintainability
- Refactoring prompts (readability, decoupling, reuse, naming)
- Review prompts (bugs, race conditions, error cases, API misuse, performance)
- Documentation & comments
- Team conventions: making AI-driven changes traceable (commits, PR descriptions)
Security, Data Protection, IP – Practical Guardrails
- What can go into prompts (and what cannot): secrets, customer data, proprietary code
- Handling dependencies: new libraries only with justification/review
- “Trust but verify”: security checks and linting
- Prompt checklist: spotting risks (injection patterns, insecure defaults, input validation)

Objectives:

Use AI as a productive pair programmer – in a controlled and reproducible way
Formulate requirements so that correct code can be produced (including edge cases)
Apply reusable prompt and workflow patterns
Accelerate testing and debugging with AI without losing quality
Use AI for refactoring/review to increase maintainability
Identify and avoid security and data protection risks in AI workflows

Target audience:

Developers (backend, frontend, full stack), tech leads, senior engineers
Development teams that want to standardize AI assistance in day-to-day work

Prerequisites:

Practical experience in software development
Familiarity with Git and basic test/build workflows
No prior AI knowledge required (will be built up in the course)

Description:

AI350 Vibe Coding for Developers is a practice-oriented developer workshop that shows how to use generative AI (LLMs) as a “coding copilot” in a way that increases speed without compromising code quality, security, and maintainability. By “Vibe Coding” we mean an iterative way of working: quickly turning ideas into running code, refining it together with the model, consistently following up with tests/checks, and hardening the result as in a real review.

The course AI350 Vibe Coding for Developers provides a robust working model for AI-assisted programming: when AI is particularly strong—and where it systematically falls short. Participants learn reusable prompt and workflow patterns that turn “Generate code for me” into a controlled engineering process: Spec-First, Plan-First, Diff-Requests, Constraint-Prompts, Test-First loops, as well as Review and Hardening prompts.

At the core is an end-to-end exercise case: the group builds or extends a small component/service/feature and uses AI throughout the entire cycle—from requirements and design through implementation to tests, documentation, code review, and hardening. By the end, participants have a personal “Vibe Coding” toolkit that can be used directly in day-to-day team work.