Course · 12 chapters
AI engineering: le fondamenta
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
La mappa dello percorso AI Engineering Foundations — cosa insegnano i chapter, come si collegano tra loro e da dove iniziare.
- 2L'arte del prompt engineering
Scrivi prompt che funzionano come programmi — strutturati, testabili e costantemente efficaci.
- 3Context engineering
Padroneggia l'arte e la scienza di curare il contesto giusto per gli AI agent.
- 4Output strutturati e schema engineering
Costruisci pipeline AI type-safe che restituiscono esattamente la forma di dati che ti serve, ogni volta.
- 5Fondamenti di RAG: dalla chat al retrieval
Costruisci la pipeline RAG minima viabile — chunk, embed, store, retrieve, augment, generate — in codice semplice.
- 6RAG engineering
Porta in produzione la struttura di retrieval — approfondimento sugli embedding, strategie di chunking, document processing, pattern avanzati e valutazione. Presuppone RAG Foundations.
- 7Fine-tuning per AI Engineer
Quando, perché e come fare fine-tuning degli LLM — dalla preparazione del dataset al deployment in produzione.
- 8Multimodal AI engineering
Costruisci sistemi in produzione con vision API, estrazione da documenti e AI multimediale.
- 9Dataset engineering
Costruisci i dataset che fanno funzionare davvero i sistemi AI — dalla generazione sintetica alle suite di eval.
- 10Prompt caching e ottimizzazione dell'inferenza
Progetta inferenza LLM più veloce, economica ed efficiente — dalla meccanica della KV cache alle strategie di serving in produzione.
- 11Context engineering per knowledge system
Architetta knowledge base che gli AI agent possono navigare, da cui possono recuperare e su cui possono agire.
- 12Post-Training: DPO, GRPO & RL per LLM
Scegli l'algoritmo di post-training giusto -- preference optimization, reasoning RL e agent RL -- senza annegare nei paper di ricerca.
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.