AI Engineer Career Path
12 modules from LLM fundamentals to system design, with quizzes, flashcards, AI-graded answers and mock interviews.
- Beginner to intermediate
- 12 modules
- Modules 1 and 2 free
This is the classic path. Your progress here is saved. The new AI Engineering Career Path is now the main course.
Modules
Module 1 of 12
LLM fundamentals
Tokens, context windows, sampling, and how models generate text.
Module 2 of 12
Prompt engineering
Writing clear instructions, examples, structure, and system prompts.
Module 3 of 12
LLM APIs and tool use
Messages APIs, streaming, and letting models call your functions.
Module 4 of 12
Structured outputs
Getting reliable JSON out of a model and validating it.
Module 5 of 12
Embeddings and vector search
Turning text into vectors and finding nearest neighbours fast.
Module 6 of 12
RAG
Retrieval-augmented generation: chunking, retrieval, reranking, grounding.
Module 7 of 12
Agents
Loops, tools, planning, memory, and when an agent is the wrong answer.
Module 8 of 12
Evals
Measuring LLM quality: test sets, graders, regressions.
Module 9 of 12
Fine-tuning (and when not to)
SFT, LoRA, preference tuning, and the cheaper alternatives.
Module 10 of 12
Cost, latency and caching
Token economics, prompt caching, batching, model routing.
Module 11 of 12
Safety and guardrails
Prompt injection, data leakage, moderation, and defence in depth.
Module 12 of 12
System design for AI apps
Putting it together: architectures for production LLM systems.