Learn AI Engineering
Everything you need to go from curious to production-ready. Pick a path below, work through the articles in order, and track your progress as you go.
LLM Fundamentals
Understand how large language models work, from tokenization to context windows to hallucination.
Prompt Engineering
Master system prompts, few-shot techniques, chain of thought reasoning, and structured output.
RAG & Retrieval
Build retrieval-augmented generation systems with embeddings, vector databases, and context engineering.
AI Agents
Build agents with tool use, memory, multi-agent orchestration, and evaluation frameworks.
Production AI
Ship AI to production with cost optimization, observability, streaming, and tool integration.
AI-Assisted Coding
Level up your dev workflow with AI coding assistants, agent skills, and practical techniques.
In-Depth Guides
Long-form references that go deep on a single topic. Read them standalone or alongside a learning path.
What Is an AI Engineer? The Role Reshaping Tech in 2026
AI engineers build production AI systems, not train models. Here's what the role involves, how it differs from ML engineers and data scientists, and what you need to break in.
How Large Language Models Work
A practical, math-free guide to how LLMs work. Tokenization, embeddings, transformer processing, and next-token prediction. Learn why prompts matter, context windows exist, and models hallucinate.
Getting Started with Prompting
A practical guide to writing prompts that get better results. Learn the fundamentals of prompt engineering.
AI Engineer Roadmap 2026: Skills, Tools, and Career Path
A complete roadmap for becoming an AI engineer in 2026. From Python fundamentals to production AI systems, here are the skills, tools, and frameworks you need at each stage.