Course · 12 chapters
AI Engineering Foundations
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
The map of the AI Engineering Foundations skill path — what each chapter teaches, how they fit together, and where to start
- 2Prompt Engineering Craft
Write prompts that work like programs — structured, testable, and consistently effective
- 3Context Engineering
Master the art and science of curating optimal context for AI agents
- 4Structured Outputs
Constrained decoding, schema engineering, and type-safe AI pipelines
- 5RAG Foundations: From Chat to Retrieval
Build the minimum viable RAG pipeline — chunk, embed, store, retrieve, augment, generate — in plain code
- 6RAG Engineering
Take the retrieval backbone to production — embeddings, chunking strategies, hybrid retrieval, advanced patterns, and evaluation. Assumes RAG Foundations.
- 7Fine-Tuning for AI Engineers
When to fine-tune vs RAG vs prompting — SFT, LoRA, dataset prep, and evaluation
- 8Multimodal AI Engineering
Vision, document, PDF, and video processing — build with images and audio, not just text
- 9Dataset Engineering
Build high-quality training and eval datasets — synthetic data, labeling, and curation pipelines
- 10Prompt Caching & Inference Optimization
Prompt caching, KV-cache, batching, speculative decoding, and token budgeting
- 11Context Engineering for Knowledge Systems
Architect knowledge bases that AI agents can navigate, retrieve from, and act upon
- 12Post-Training: DPO, GRPO & RL
Pick the right post-training algorithm — DPO, GRPO, Dr-GRPO, DAPO, KTO, GiGPO — without drowning in research papers
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.