Course · 12 chapters
Fundamentos de ingeniería de IA
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
- 1Fundamentos de Ingeniería de IA: Introducción
El mapa del skill path Fundamentos de Ingeniería de IA: qué enseñan los capítulos, cómo encajan entre sí y por dónde empezar.
- 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 & Schema Engineering
Build type-safe AI pipelines that return exactly the data shape you need, every time.
- 5Fundamentos de RAG: del chat a la recuperación
Construye el pipeline de RAG mínimo viable —fragmentar, incrustar, almacenar, recuperar, aumentar, generar— en código sencillo.
- 6RAG Engineering
Take the retrieval backbone to production — embeddings deep-dive, chunking strategies, document processing, advanced patterns, and evaluation. Assumes RAG Foundations.
- 7Fine-Tuning for AI Engineers
When, why, and how to fine-tune LLMs -- from dataset preparation to production deployment.
- 8Multimodal AI Engineering
Build production systems with vision APIs, document extraction, and multimedia AI.
- 9Dataset Engineering
Build the datasets that make AI systems actually work — from synthetic generation to eval suites.
- 10Prompt Caching & Inference Optimization
Engineer faster, cheaper, and more efficient LLM inference — from KV-cache mechanics to production serving strategies.
- 11Context Engineering for Knowledge Systems
Architect knowledge bases that AI agents can navigate, retrieve from, and act upon.
- 12Post-Training: DPO, GRPO & RL for LLMs
Pick the right post-training algorithm -- preference optimization, reasoning RL, and agent RL -- 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.