Beginner tierLLM Engineer for Beginners
No IT background needed. Prompting, RAG, agents, fine-tuning, evaluation, observability and security: thirteen lessons from “what is a large language model?” to shipping LLM products that hold up in production.
✓ You already own this bootcamp. It’s waiting in your Active Bootcamps.
Prompt → completion
LLM engineers turn models into products that actually work in production.
Next token · probability
By the numbers
Every model ships with a card that says what’s inside and what it’s for. Here’s ours. Everything on it unlocks the moment you enroll.
The job
Anyone can call a model API. The LLM engineer builds everything around that call, so a real user gets an answer that is grounded, safe, fast and affordable — and then proves it with evaluations and traces.
Connect models to your company’s data with retrieval, give them tools, and chain the steps into agents.
Write evaluations, trace every request, watch cost and latency, and catch quality drift before users do.
Block prompt injection, filter what goes in and out, and keep private data private.
Who it’s for
Eleven test cases, judged against what’s actually in the syllabus.
Scores are illustrative. The reasons aren’t: each one comes from the syllabus.
Curriculum
Each dot is a chapter and each cluster a lesson, with related lessons placed close together, the way an embedding model groups similar meaning. Pick a lesson to read every chapter and section title.
Positions are hand-arranged to group related lessons; hover a dot for its chapter.
Inside every chapter
Core chapters are split the same way: five deep-dive reading sections, a hands-on practice set and a quiz with an explained answer key. Here’s a real one, 2.3 Text Chunking Strategies, chunked by section. Pick a chunk.
overlap — a wink at Section C: chunk boundaries share context
Section A · Lesson 2.3
What text chunking is, the context-window constraint, why mixed-topic chunks make blurry embeddings, and the three classic ways bad chunking breaks a RAG system.
Toolbelt
The providers, frameworks, vector databases and eval tools you’ll actually use, grouped the way you’d store them in a vector index, with the lessons that teach them.
Lesson 11 · Your lab
Nine install guides, starting with Python, Git and VS Code on Windows, macOS or Linux, then provider SDKs and API keys, frameworks, vector databases and eval tooling. The last chapter shows Killercoda: free browser sandboxes for when you can’t install anything.
Lesson 12 · Capstones
Each capstone hands you a fictional company with a realistic problem. You work it in three phases, then compare your plan with a reference solution that explains each key decision and the alternative it rejected.
Production RAG support assistant
A project-management SaaS grew from 4,000 to over 40,000 customer accounts in three years. Its 45-person support team can’t keep up: first response slipped from under two hours to over fourteen. Support wants an AI assistant that doesn’t make things up.
Multi-agent workflow automation
A manufacturing and logistics group with about 18,000 employees across 40 sites runs on SAP, ServiceNow, Workday and SharePoint, barely connected. Onboarding a new vendor takes 19 days and five manual handoffs between Procurement, Finance and Legal.
Evaluation & safety-guardrail system
A regulated bank built an internal copilot for 6,000 employees, then Compliance blocked the launch. Before it ships, the copilot needs a formal evaluation and guardrail framework that would satisfy FCA- and BaFin-style supervision.
Lesson 13 · Portfolio
Eight modules turn your capstone work into a public portfolio. One of them is the document type that sets LLM engineers apart: an evaluation report and system card for something you built.
Salaries
Gross annual base salary, by experience, from public salary data. Pick a country.
Indeed’s reported machine-learning engineer salaries in Germany average €70.8k (€49.4k–€101.5k). jobrise.io puts Berlin AI engineers at €55–75k junior, €75–100k mid and €100–135k senior, with LLM specialists at the top of the senior band.
Sources: Indeed Germany (Sep 2026), jobrise.io (Jun 2026), PayScale Germany (Feb 2026).
Dutch salaries are usually quoted per month, so these are monthly figures × 12; most contracts add an 8% holiday allowance on top. Indeed’s 234 reported machine-learning engineer salaries average €4,482 a month (€2,888–€6,957). AI teams at large international companies in Amsterdam pay above these ranges.
Source: Indeed Netherlands (Sep 2026).
Belgian data is thin: PayScale averages €47.2k for machine-learning engineers (19 profiles, €28k–€82k), and Indeed has only a handful of reports. Packages usually add a 13th month, double holiday pay and meal vouchers.
Sources: PayScale Belgium (Jun 2026), Indeed Belgium (Sep 2026).
Few salary sites track “LLM engineer” as a title yet, so these ranges use machine-learning- and AI-engineer data. Curves show where each band sits, not exact distributions.
Mentor Bob
Self-paced doesn’t mean on your own. Mentor Bob is an AI study assistant built into every section of the bootcamp. Its context is the section you have open, so you can ask it to explain something another way, give a different example or help with an error — any time.
Included free — not an upsell
You
Should I just split my documents every 500 tokens?
Mentor Bob
You can, and it’s the simplest place to start: this section calls it fixed-size chunking. Add some overlap so a sentence cut at a boundary still appears whole in one chunk. The weakness is that it ignores meaning, so a cut can land mid-thought. If retrieval suffers, try sentence-boundary chunking next, then semantic chunking, which splits where the similarity between neighbouring sentences drops below a threshold.
FAQ
The things people ask before they enroll.
No IT background is required. The examples are in Python and the course doesn’t teach the language from zero, so being able to read simple Python helps. Lesson 11 walks you through installing Python, Git and VS Code.
Most lessons call hosted model APIs, so an ordinary laptop is enough. The open-source fine-tuning chapters in Lesson 4 explain what hardware those techniques need.
The bootcamp price doesn’t include model-provider costs. Lesson 1 covers token economics, rate limits and cost management before you build anything big, and Lesson 11 shows how to set up API keys.
OpenAI, Anthropic, Google, Meta and others, plus open-source models, along with how to choose between hosted APIs, self-hosting and fine-tuning.
No. Everything is written: 1,067 sections with explanations, examples, code and exercises that you can search back through any time.
There’s no deadline and access never expires. The core is 150 chapters, each with five long reading sections, practice and a quiz, so plan in months rather than weeks.
An AI study assistant in the corner of every lesson page. It reads the section you have open, so you can ask it to re-explain, give a different example or help with an error. It’s an LLM, so check anything important against the lesson.
No course can promise that. You do get hands-on skill across the whole LLM stack, three capstones worked end to end, and Lesson 13’s process for turning them into a portfolio, including an evaluation report and system card.
Yes. You pay €379 once and keep access for life. There’s no subscription.
This bootcamp is about building applications on top of language models. MLOps Fundamental covers pipelines for training and deploying ML models, and AIOps Fundamental applies AI to IT operations. Both are Fundamental tier and assume prior experience.
Enroll
Secure checkout with Stripe. Everything unlocks as soon as you’ve paid.