Learning materials
Practical AI Engineering lessons in markdown.
Filter by topic, difficulty, or keyword. Each lesson includes technical reading structure, code-friendly examples, and a related practice quiz.
10 lessons
AI Engineering Roadmap
A practical roadmap for learning AI Engineering from software fundamentals through RAG, agents, LLMOps, and system design.
Agentic AI Fundamentals
Learn how agentic AI systems use goals, tools, planning, memory, and guardrails to complete multi-step workflows.
RAG Systems Explained
Understand Retrieval-Augmented Generation systems from ingestion and chunking through retrieval, reranking, grounding, and evaluation.
LLMOps Basics
Learn the operating discipline for LLM products: evaluation, monitoring, release management, feedback loops, and cost controls.
Prompt Engineering for Production
Learn how to write prompts that are structured, testable, observable, and useful inside production software.
Vector Databases Explained
Learn what vector databases do, how embeddings power similarity search, and how metadata filters and hybrid retrieval improve RAG.
AI Evaluation and Guardrails
Learn how to evaluate AI outputs and design guardrails that reduce quality, safety, and product risk.
MCP for AI Engineers
Understand the Model Context Protocol and how it helps AI applications connect to tools, resources, and structured context.
Fine-Tuning vs RAG
Learn when to use fine-tuning, RAG, prompting, or a combination for AI Engineering problems.
AI System Design Interview Basics
Prepare for AI system design interviews by practicing requirements, architecture, tradeoffs, evaluation, and production constraints.
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