AI Engineering Foundations

The map of the AI Engineering Foundations skill path — what the chapters teach, how they fit together, and where to start.

Write prompts that work like programs — structured, testable, and consistently effective.

Master the art and science of curating optimal context for AI agents.

Build type-safe AI pipelines that return exactly the data shape you need, every time.

Build the minimum viable RAG pipeline — chunk, embed, store, retrieve, augment, generate — in plain code.

Take the retrieval backbone to production — embeddings deep-dive, chunking strategies, document processing, advanced patterns, and evaluation. Assumes RAG Foundations.

When, why, and how to fine-tune LLMs -- from dataset preparation to production deployment.

Build production systems with vision APIs, document extraction, and multimedia AI.

Build the datasets that make AI systems actually work — from synthetic generation to eval suites.

Engineer faster, cheaper, and more efficient LLM inference — from KV-cache mechanics to production serving strategies.

Architect knowledge bases that AI agents can navigate, retrieve from, and act upon.

Pick the right post-training algorithm -- preference optimization, reasoning RL, and agent RL -- without drowning in research papers.