Training course · Vibe Coding · AI Engineering & LLM Applications

Vibe Coding for AI Engineers

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 AI Engineering & LLM Applications. 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.

AI engineering is the discipline of turning language models into dependable products: chat assistants, document analysis, retrieval-augmented generation, agents with tool access and AI features inside existing applications. The code around the model is often more demanding than the model call itself, because prompt versioning, structured output, evaluation, cost control, latency, observability and protection against prompt injection decide success in production. AI coding assistants and agents accelerate exactly this work, from building evaluation pipelines and tool definitions to MCP servers and root-cause analysis in traces. In this course you learn to develop LLM applications systematically with AI support, make their quality measurable and run them securely, cost-effectively and in compliance with regulation.

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?

  • Software developers integrating LLM features into products
  • AI engineers and machine learning engineers
  • Data scientists focusing on generative AI
  • Backend and platform teams providing AI infrastructure
  • Technical product owners for AI features

Typical challenges we address

  • Prototypes shine in demos but deliver inconsistent quality in production
  • Without systematic evaluation, changes to prompts and models are risky
  • Costs, latency and rate limits spiral as usage grows
  • New security risks from prompt injection, data leakage and over-privileged agents

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.
  • Build RAG applications, agents and LLM features with AI coding tools systematically and to production standard.
  • Make the quality of LLM applications measurable and manageable with automated evaluations.
  • Run LLM applications securely and cost-effectively, with observability, prompt injection defences and clear permissions.

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: AI Engineering & LLM Applications
  • Building RAG pipelines
  • Automating evaluation
  • Developing agents and tools
  • Securing structured output
  • Observability and cost
  • Defending against prompt injection
Module 7 Data protection, quality & transfer to daily work
  • LLM applications often process personal and confidential data: you learn to assess EU AI Act requirements on transparency and risk classes, design GDPR-compliant data flows with processing agreements and deletion policies, manage API keys securely and respect the licence terms of models and datasets.
  • 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

Building RAG pipelines

Implement ingestion, chunking, embeddings, vector search, hybrid retrieval and reranking, and generate answers with citations while honouring the access rights of source documents at retrieval time.

Automating evaluation

Create test sets with expected answers, define metrics for relevance, faithfulness and format, calibrate LLM-as-a-judge scoring and run evaluations in CI on every prompt or model change.

Developing agents and tools

Design tool definitions and MCP servers, implement agent loops with planning, tool calls and stop conditions, and build in permissions and human approval for consequential actions.

Securing structured output

Define JSON schemas for model responses, validate output, implement retry strategies on failure and make extraction tasks such as invoice or contract data robust. Faulty responses never reach downstream systems.

Observability and cost

Capture traces, token usage, latency and error rates, analyse problematic conversations and cut costs through caching, model routing and context reduction. Budgets and alerts per feature prevent surprises.

Defending against prompt injection

Test attack scenarios via manipulated documents, web pages or tool results, filter inputs and outputs, limit agent permissions and make red-teaming tests a fixed part of the pipeline.

Possible hands-on projects

Knowledge assistant with sources

A RAG chat over internal documents with citations, feedback buttons and an evaluation page that tracks answer quality across test questions.

Evaluation harness

A command-line tool that runs prompt variants against a test set, scores the results and clearly shows differences between versions.

Document extractor

A service that accepts PDFs, extracts structured data against a JSON schema, validates it and flags uncertain cases for manual review.

Prerequisites

Basic programming knowledge and some Git experience are helpful; you should be able to read simple code and make sense of error messages. You have solid programming skills, ideally in Python or TypeScript, and experience with APIs; prior exposure to LLM APIs helps but is not required.

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

LLM applications often process personal and confidential data: you learn to assess EU AI Act requirements on transparency and risk classes, design GDPR-compliant data flows with processing agreements and deletion policies, manage API keys securely and respect the licence terms of models and datasets.

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 Hanover Request
  • Mon, 14 December 2026 – Tue, 15 December 2026 Online Request
  • Thu, 14 January 2027 – Fri, 15 January 2027 Munich Request
  • Tue, 19 January 2027 – Wed, 20 January 2027 Online Request
  • Tue, 2 February 2027 – Wed, 3 February 2027 Stuttgart 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.

How does AI engineering differ from classic machine learning?

In classic machine learning you train your own models on your own data. In AI engineering you usually work with pre-trained language models via APIs or self-hosted models and focus on context, retrieval, tools, evaluation and operations. Statistics and training know-how matter less, while software engineering skills matter all the more. That is precisely where this course comes in.

How do we know whether our LLM application is good enough?

With an evaluation set of realistic questions and expected results, a mix of rule-based checks, LLM-as-a-judge and human spot checks, plus monitoring in production. The key is running evaluations automatically on every change. In the course you build such a setup and learn to calibrate judge models.

How much does “Vibe Coding for AI Engineers” 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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