Training course · Claude Code · AI Engineering & LLM Applications

Claude Code for AI Engineers

Agentic coding with Claude Code in the terminal and IDE: CLAUDE.md, subagents, hooks, MCP, CI pipelines and secure permissions.

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 Claude Code 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.

Claude Code is Anthropic’s agentic coding assistant, working directly in your terminal and in popular IDEs. Rather than completing individual lines, it reads your repository, plans changes across multiple files, runs commands, executes tests and prepares commits. Over two days you will learn to use it with deliberate control: clear project instructions in CLAUDE.md, careful context management, and a workflow in which you set the direction as a developer and can trace every change the agent makes back to a clear intention.

The course is built around real engineering work: diagnosing bugs, delivering features, refactoring legacy code and filling gaps in test coverage. You will configure subagents for specialised tasks, enforce quality checks with hooks, connect external systems through the Model Context Protocol (MCP) and capture team knowledge as reusable skills. Finally, you will run Claude Code headless in CI pipelines and define permissions, sandboxing and approval rules so that its use remains defensible even in security-sensitive environments.

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 use Claude Code for multi-step development tasks while keeping full control over every change.
  • You maintain a CLAUDE.md that effectively conveys project conventions, commands and architectural guidance to the agent.
  • You extend Claude Code with subagents, hooks, MCP servers and skills tailored to your team.
  • You integrate Claude Code into Git workflows and CI pipelines with traceable, test-backed results.
  • 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 How Claude Code works
  • Installation, sign-in and use in the terminal, VS Code and JetBrains IDEs
  • Understanding the agentic loop: read, plan, act, verify
  • Using plan mode to agree on an approach before any code changes
  • Writing effective task briefs with goal, context and acceptance criteria
Module 2 Project context with CLAUDE.md and context management
  • Structuring CLAUDE.md at project, directory and user level
  • Documenting build, test and style conventions so the agent follows them reliably
  • Managing the context window with targeted file references, compaction and fresh sessions
  • Resuming sessions and breaking long tasks into manageable stages
Module 3 Extending Claude Code with subagents, hooks, MCP and skills
  • Defining subagents for code review, research or test generation
  • Using hooks to enforce formatting, linting or checks before and after tool calls
  • Connecting MCP servers for issue trackers, databases or internal documentation
  • Creating custom slash commands and skills for recurring team workflows
Module 4 Git workflows, testing and quality assurance
  • Creating branches, meaningful commits and pull requests with Claude Code
  • Test-first work: have tests written before the implementation
  • Reviewing diffs systematically and rejecting or refining changes
  • Organising parallel work with Git worktrees and multiple sessions

Day 2

Module 5 Headless operation, CI and security
  • Running Claude Code non-interactively in scripts and CI pipelines
  • Automated reviews and issue handling in GitHub or GitLab workflows
  • Configuring permission modes, allow and deny rules, and sandboxing
  • Handling secrets, prompt injection risks and organisation-wide policies
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

You have hands-on programming experience in at least one language and are comfortable with Git and the command line. 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 admin rights, Git, a Node.js runtime and an up-to-date editor; a Claude subscription or Anthropic API access is required for the exercises.

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.

  • Mon, 23 November 2026 – Tue, 24 November 2026 Online Request
  • Mon, 30 November 2026 – Tue, 1 December 2026 Augsburg Request
  • Tue, 15 December 2026 – Wed, 16 December 2026 Online Request
  • Thu, 14 January 2027 – Fri, 15 January 2027 Online Request
  • Mon, 18 January 2027 – Tue, 19 January 2027 Kaiserslautern Request
  • Mon, 1 February 2027 – Tue, 2 February 2027 Online Request

Frequently asked questions

How is Claude Code different from traditional code completion?

Code completion suggests the next few lines, whereas Claude Code acts as an agent that handles whole tasks: it searches the repository, plans changes across several files, runs tests and commands, and corrects itself based on the results. You define the goal and review the outcome instead of steering every single line yourself.

Is Claude Code secure enough for enterprise use?

By default, Claude Code asks for approval before editing files or running commands. Permission rules, sandboxing, managed settings and hooks let you define precisely what the agent may do. During the course you learn to configure these mechanisms to match your security and data protection requirements and to assess risks such as prompt injection.

Does Claude Code work with my language and tech stack?

Yes. Claude Code is language-agnostic because it works with files, terminal commands and your existing build and test tooling. Whether you use Python, Java, TypeScript, Go, SAP-related code or infrastructure as code, what matters most is a well-written CLAUDE.md describing your commands and conventions. Each course variant adds the relevant domain focus.

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 “Claude Code 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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