AI RAG Engineering
Learn to build production-ready Retrieval-Augmented Generation (RAG) systems that intelligently combine large language models with contextual knowledge.
What you will learn in this course
WHY THIS COURSE IS A GAMECHANGER: Most engineers treat RAG as a black box – they may understand the theory but struggle to architect, optimize, and deploy RAG systems that work reliably in production. This course transforms RAG from abstract concepts into tangible, deployable skills. You'll build a complete chatbot system from scratch, understanding every component of the RAG pipeline.
ABOUT THIS COURSE: Instead of abstract examples, you're building a Chatbot – a real RAG system that answers customer questions by retrieving and synthesizing information from actual website content. By the end of the course, you'll have a working prototype that you can deploy. You'll learn abbout data ingestion, embeddings & vector search, LLM integration, security & quality controls, and Docker deployment.
Agenda
RAG Fundamentals & Architecture
Data Foundation & Embeddings
Advanced Retrieval
LLM Integration & Chat Backend
Security & Production Readiness
Deployment
audience
This course is designed for
- Software Engineers wanting to understand RAG systems
- DevOps Professionals deploying AI systems
- AI/ML Practitioners building production applications
- Anyone interested in AI development
prerequisites
To get most out of this course, you should have:
- Basic Python knowledge (familiar with functions, classes, libraries)
- Understanding of APIs (REST, HTTP basics)
- Comfortable using AI tools (Copilot or similar)
style
Our trainers have years of experience and will deliver the right mix of:
- Inhalte mit echten Szenarien aus dem Entwickleralltag
- Praktische Übungen in Form von Hands on Labs
- Offizielle Trainingspartner (Mondoo & IBM HashiCorp)
- Lebenslanger Zugriff auf Kursunterlagen
- Update Infos auch nach dem Workshop
- Expert:innen mit echter Projekterfahrung
Technical requirements
We recommend the following equipment:
- Stable internet connection
- Access to AI tools and services (e.g., OpenRouter, Groq, OpenAI API) (Free accounts may need to be created)
- Browser compatible with modern web applications (e.g., Chrome)
- If you're eager to try out demonstrated tools on your own: Code editor (e.g., VS Code) with sufficient hardware (GPU) and a Python installation
Benefits
Multiple
Hands On Labs
Personal Certificate
Virtual Classroom
Own Lab-Environment
Need more than training?
Training is often a first step. Many teams realize they need support beyond the course to make things work in production, speed up the process or bridge short-term bottlenecks..
We can support you beyond training, through hands-on consulting, project collaboration, or as an embedded enablement team, we are helping you apply what you learned, validate decisions, and move forward with confidence.
Why Infralovers is your perfect partner
DE | EN
We speak two languages
100%
Real world experiences
10+
Years of experience
1500+
Training hours held per year
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