AI Engineering Course — 26 Weeks

A structured, week-by-week course to transition into AI engineering. Derived from "How To Break Into AI Engineering (2026 Edition)" by The AI Engineer — a free, 34-page guide with 5 learning tracks, 70+ curated resources, and 3 transition roadmaps.

This course takes that guide and turns it into an actionable 26-week plan with weekly study material, build assignments, and checkpoints.

Who This Is For

Starting Point	Timeline	What You're Adding
Software Engineer	6 months	LLM APIs, RAG, agents, evaluation
ML Engineer	2–3 months	Prompt engineering, RAG patterns, agent architectures
Data Scientist	6–9 months	Production engineering first, then the full AI stack

The default path follows the Software Engineer → AI Engineer roadmap (26 weeks). Abbreviated paths for ML Engineers and Data Scientists are included.

Course Structure

Phase 1: Make an LLM Do Something Useful (Weeks 1–8)

Week	Focus
01	First API Call + Environment Setup
02	Prompt Engineering Fundamentals
03	Advanced Prompting + Structured Outputs
04	Embeddings + Vector Storage
05	RAG Pipeline v1 — Naive Implementation
06	RAG Pipeline v2 — Chunking & Retrieval Strategies
07	RAG Pipeline v3 — Production Patterns
08	Checkpoint: RAG Portfolio Project

Phase 2: Make It Production-Grade (Weeks 9–16)

Week	Focus
09	Function Calling + Tool Use Primitives
10	Agent Frameworks Survey
11	Multi-Step Agents + MCP
12	Agentic RAG
13	Agent Deep Dive — Read the Source Code
14	Checkpoint: Agent Project
15	Evaluation Fundamentals (DeepEval)
16	RAG Evaluation (RAGAS)

Phase 3: Understand the Machine (Weeks 17–20)

Week	Focus
17	Transformer Architecture — Attention & GPT
18	Training, Tokenization & Fine-Tuning Decisions
19	LLM Internals — Visual Deep Dive
20	Reasoning & Inference-Time Scaling (optional)

Phase 4: Security & Hardening (Weeks 21–24)

Week	Focus
21	OWASP Top 10 for LLM Applications
22	Red-Teaming with PyRIT
23	Offensive Security + Deepteam
24	Checkpoint: Full Eval Suite

Phase 5: Ship It (Weeks 25–26)

Week	Focus
25	Production Deployment + Monitoring
26	Final Checkpoint: Portfolio Piece + Interview Prep

Key Resources

All resources are curated by the original guide. Each was filtered for: updated in the last 12 months, targets engineers with production experience, and framework-agnostic or tied to a framework with real adoption.

Resource	Stars	Track
rasbt/LLMs-from-scratch	~89K	LLM Fundamentals
mlabonne/llm-course	~76K	LLM Fundamentals
dair-ai/Prompt-Engineering-Guide	~72K	RAG & Retrieval
openai/openai-cookbook	~60K	RAG & Retrieval
NirDiamant/RAG_Techniques	~26K	RAG & Retrieval
huggingface/smolagents	~26K	AI Agents
huggingface/agents-course	~25K	AI Agents
anthropics/courses	~19K	AI Agents
vllm-project/vllm	~75K	Hardware & Inference
confident-ai/deepeval	~14K	Security & Evaluation
explodinggradients/ragas	~13K	Security & Evaluation
Azure/PyRIT	~3.4K	Security & Evaluation

Full resource list with specific chapters/sections in course/README.md.

Credits

This course is derived from "How To Break Into AI Engineering (2026 Edition)" by The AI Engineer. The original guide is 100% free. All repository recommendations, study sequences, and checkpoints are credited to the original authors. Star counts as of March 2026.