Course · 12 chapters
AI Engineering: Grondslagen
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
De kaart van het skilpad AI engineering foundations — wat de hoofdstukken behandelen, hoe ze samenhangen en waar je begint.
- 2Prompt engineering als vak
Schrijf prompts die werken als programma's — gestructureerd, testbaar en consistent effectief.
- 3Context engineering
Beheers de kunst en wetenschap van het samenstellen van optimale context voor AI-agents.
- 4Structured Outputs & Schema Engineering
Bouw typeveilige AI-pipelines die elke keer precies de datastructuur retourneren die je nodig hebt.
- 5RAG-fundamenten: van chat naar retrieval
Bouw de minimaal werkbare RAG-pipeline — chunk, embed, opslaan, ophalen, aanvullen, genereren — in gewone code.
- 6RAG engineering
Breng de retrieval-backbone naar productie — diepgaande embeddings, chunking-strategieën, documentverwerking, geavanceerde patronen en evaluatie. Vereist RAG Foundations.
- 7Fine-Tuning voor AI Engineers
Wanneer, waarom en hoe je LLM's fine-tunet -- van datasetvoorbereiding tot productie-deployment.
- 8Multimodale AI-engineering
Bouw productiesystemen met vision-API's, documentextractie en multimedia-AI.
- 9Dataset engineering
Bouw de datasets die AI-systemen écht laten werken — van synthetische generatie tot evaluatiesuites.
- 10Prompt Caching & Inference-optimalisatie
Engineer snellere, goedkopere en efficiëntere LLM-inference — van KV-cache-mechanismen tot productie-servingstrategieën.
- 11Context engineering voor kennissystemen
Ontwerp kennisbanken waar AI-agents doorheen kunnen navigeren, uit kunnen ophalen en naar kunnen handelen.
- 12Post-Training: DPO, GRPO & RL voor LLMs
Kies het juiste post-training algoritme -- preference optimization, reasoning RL en agent RL -- zonder te verdrinken in onderzoekspapers.
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.