Course · 12 chapters
KI-Engineering-Grundlagen
Ship AI features to production: prompting, RAG, structured outputs, fine-tuning, and inference tuning. Hands-on, free, 12 chapters (~4.3h) for engineers.
What you'll be able to do
- Write structured, testable prompts
- Build a RAG pipeline end to end
- Generate type-safe structured outputs
- Fine-tune models with LoRA
- Cut AI latency and cost
- Build multimodal AI pipelines
What's inside
- 1AI Engineering Foundations: Intro
Die Karte des AI Engineering Foundations Skill Path — was die Kapitel vermitteln, wie sie zusammenhängen und wo du anfangen solltest.
- 2Die Kunst des Prompt Engineering
Schreibe Prompts, die wie Programme funktionieren — strukturiert, testbar und durchgehend effektiv.
- 3Context Engineering
Die Kunst und Wissenschaft, optimalen Kontext für KI-Agenten zu kuratieren.
- 4Structured Outputs & Schema Engineering
Baue typsichere KI-Pipelines, die jedes Mal exakt die Datenstruktur liefern, die du brauchst.
- 5RAG-Grundlagen: Vom Chat zum Retrieval
Baue die minimale RAG-Pipeline — chunken, embedden, speichern, abrufen, augmentieren, generieren — in schlichtem Code.
- 6RAG Engineering
Bringe das Retrieval-Rückgrat in die Produktion — Embeddings-Deep-Dive, Chunking-Strategien, Dokumentenverarbeitung, fortgeschrittene Patterns und Evaluation. Setzt RAG Foundations voraus.
- 7Fine-Tuning für AI Engineers
Wann, warum und wie du LLMs fine-tunest – von der Dataset-Vorbereitung bis zum Production-Deployment.
- 8Multimodales AI Engineering
Baue Produktionssysteme mit Vision-APIs, Dokumentenextraktion und multimedialer KI.
- 9Dataset Engineering
Erstelle die Datasets, die KI-Systeme tatsächlich funktionieren lassen — von synthetischer Generierung bis zu Eval-Suiten.
- 10Prompt-Caching & Inferenz-Optimierung
Mache LLM-Inferenz schneller, günstiger und effizienter — von der KV-Cache-Mechanik bis zu Serving-Strategien in der Produktion.
- 11Context Engineering für Wissenssysteme
Architektiere Wissensbasen, die KI-Agenten navigieren, abrufen und darauf handeln können.
- 12Post-Training: DPO, GRPO & RL für LLMs
Wähle den richtigen Post-Training-Algorithmus – Preference Optimization, Reasoning RL und Agent RL – ohne in Research-Papers zu ertrinken.
Frequently asked questions
- What will I learn in AI Engineering Foundations?
- You learn to build production AI systems: prompt and context engineering, structured outputs, RAG pipelines from chunking to retrieval, fine-tuning with SFT and LoRA, multimodal processing, dataset engineering, inference optimization, and post-training methods like DPO and GRPO. Every chapter is hands-on and grounded in plain code.
- Who is this course for?
- It is built for software engineers and developers who want to ship real AI features, not just experiment with chatbots. It assumes you can read and write code and want to understand the techniques behind RAG, fine-tuning, and structured generation.
- Do I need prior AI or machine learning experience?
- You need general programming experience, but no prior machine learning background. The path is set at practitioner level and explains each technique, such as embeddings, retrieval, and LoRA, as you build with it.
- How long is the course, and how is it structured?
- The path has 12 chapters totaling about 4.3 hours (257 minutes). It opens with an intro chapter that maps every topic so you know where to start and how the chapters connect, then moves through prompting, RAG, fine-tuning, multimodal, and inference optimization.
- Is AI Engineering Foundations free?
- Yes, this path is free. You get all 12 chapters covering prompting, RAG, fine-tuning, multimodal AI, and inference optimization at no cost.
Earn a certificate
Complete all chapters to receive your certificate of completion.