Are you ready to be an LLM Agent Architect?Free LLM Agent Architect skill check.
The LLM Agent Architect skill check is five multiple-choice questions, one each on LLM fundamentals, RAG, Inference controls, AI safety and Embeddings. It is free, takes about three minutes, and grades you from Novice to Expert with every answer explained.
- 5 questions
- About 3 minutes
- Free
Choose a course
Every course in the AI Tutor has its own check: five questions, one per topic.
Career tracks 11 courses
For a new role: engineer, scientist, analyst or builder.
Sign up free to start. Your answers are graded when you finish and saved to your record.
The LLM Agent Architect skill check.Five topics, one question each.
Question 1 is below exactly as the check asks it. The answer, and why it is right, come with your result.
LLM Agent Architects design autonomous AI systems that can reason, use tools, and complete complex multi-step tasks. It's one of the newest and fastest-growing roles in AI engineering, sitting at the intersection of prompt engineering, system design, and AI safety.
This diagnostic tests five areas fundamental to agent architecture: how LLMs handle context, retrieval-augmented generation for grounding agents in real data, inference parameter tuning for reliable agent behaviour, prompt injection defence (critical for agents with tool access), and embeddings for semantic retrieval.
Three minutes, five questions — find out if you have the foundations to build autonomous AI systems.
- LLM fundamentals
Agents are limited by their context window: understanding this constraint drives architecture decisions.
- RAG
Agents need retrieval to access information beyond their training data.
- Inference controls
Deterministic agent behaviour requires careful temperature and sampling configuration.
- AI safety
Agents with tool access are high-risk targets for prompt injection: safety is non-negotiable.
- Embeddings
Semantic search powers the memory and retrieval systems that make agents useful.
What does a large language model do at each step as it writes a reply?
- ALooks the answer up in a database
- BPredicts the next token from the text so far
- CRuns a web search for every sentence
- DCopies a sentence from its training data
“Learn AI” means three things.This check tests one of them.
It tests the LLM layer an agent architect designs on: context, retrieval, sampling, prompt-injection defence and embeddings.
- 01
Machine learning foundations
How models learn from data: the statistics, training and evaluation underneath them.
For you ifyou want to train and judge models, as an ML engineer or data scientist.
Take the Machine Learning check - 02This check
Applied LLM engineering
Building software on models like Claude and GPT: prompts, retrieval, agents, evals and shipping.
For you ifyou write code and want to build AI features, not train models from scratch.
- 03
AI for your job
Using AI tools well in the work you already do: briefing them, checking what comes back, keeping data safe.
For you ifyou do not plan to code and want AI to make the work you do faster and better.
Take the AI for Operations check
Three steps.About three minutes.
It is free and nothing is booked. Save the report to keep it on your account.
- 01
Pick a course
Any of the 21 courses in the AI Tutor, from Generative AI to AI for Teachers. Each check covers five topics.
- 02
Answer five questions
One question per topic, four options each. Nothing is marked until you finish.
- 03
See your readiness
Your readiness out of 10, a skill map, every answer explained, and the path through the course.
Programmes that build on this check.Your result shows where to start.
Each one teaches LLM Agent Architect by building, and ends in deployed work graded against a rubric you can read.
Before you start.Three quick answers.
What is an LLM Agent Architect?
Someone who designs AI systems where language models autonomously use tools, make decisions, and complete multi-step tasks — think AI assistants, coding agents, and research agents.
How is this different from the Generative AI check?
Same foundational questions, because agent architecture builds on LLM fundamentals. The full courses diverge into tool use, planning, memory systems, and multi-agent orchestration.
What experience do I need?
Programming experience helps, but this quick check tests conceptual understanding. The full course teaches you to build agents from scratch.
Rather talk to a person?
Tell us what you want to do with AI. We’ll suggest a programme on a 15-minute call.