Questions
AI engineering course FAQs
What is the best way to learn AI engineering?
Follow a structured path in order: machine-learning foundations, deep learning, the Transformer, how LLMs generate text, fine-tuning and alignment, prompting and context, RAG, agents, inference, evaluation, safety, and finally system design. The AI Engineer Bootcamp is organised exactly that way, and every lesson ends with a quiz that checks you understood it.
Is this course free?
Every lesson and quiz is open to read right now, and the Temperature lab is free to try. The other interactive labs open with a paid track; the pricing page lists the three tracks. You can learn as a guest with progress saved in your browser, or sign in to sync it across devices.
Do I need a machine-learning background?
No. The course starts from the very basics. Basic programming (preferably Python) and high-school math are enough; everything else is explained inside the lessons.
How do the quizzes work?
Every lesson ends with 5 multiple-choice questions. Answer all five and submit. If you get 4 or more right, the lesson is marked as passed. If not, each question shows an explanation, and you can try again with the options shuffled.
How long does it take to finish?
Most lessons take 20–40 minutes including the interactives, practice lab and quiz; the whole bootcamp is about 63 hours of material. At one or two lessons a day, the full course takes around three to four months. Understanding each concept deeply matters more than speed.
What is the difference between an AI engineer and a machine-learning engineer?
A machine-learning engineer mostly trains, tunes and deploys models. An AI engineer mostly builds products and systems on top of existing models, especially LLMs, using prompting, context engineering, RAG, agents, fine-tuning, inference optimisation and evaluation. The two overlap, and this course covers the foundations both need.
What skills does an AI engineer need?
How LLMs work inside (Transformers, attention, tokenization); how to adapt them (prompting, context engineering, fine-tuning, LoRA); how to give them knowledge (RAG, vector search); how to make them act (agents, function calling, MCP); how to run them efficiently (inference, quantization, serving); how to measure and secure them (evaluation, observability, guardrails); and how to design complete systems.
Does the course cover AI agents and agentic AI?
Yes. Module 11 covers agents in depth (function calling, the agent loop, ReAct, plan-and-execute, reflection, memory, MCP, skills, multi-agent systems, subagents, orchestration and computer-use agents), and Module 12 covers agentic engineering and frameworks such as LangChain, LangGraph, Claude Code and Cursor.
Does it cover RAG?
Yes. Module 10 goes from vector databases and approximate nearest-neighbour search to semantic and hybrid search, rerankers, ColBERT, chunking, HyDE, caching, agentic RAG, GraphRAG and vectorless RAG.
Does it cover LLM inference optimisation?
Yes. Module 13 covers prefill vs decode, disaggregation, the KV cache and its compression, paged attention, continuous batching, speculative decoding (n-gram, Medusa, EAGLE), quantization, GGUF, llama.cpp, vLLM, SGLang and TensorRT-LLM.
Will this help me with AI engineering interviews?
Yes. The course covers the concepts asked in AI engineer, GenAI engineer, LLM engineer and ML engineer interviews, and Module 19 is dedicated to interview preparation, including a worked system-design answer.
Where does the curriculum come from?
The AI Engineer Bootcamp covers 19 core modules drawn from the established body of AI engineering knowledge — from ML fundamentals through inference optimization and agent systems. All lesson text, interactive widgets, code walkthroughs and quizzes are written specifically for this platform.