Start building with language models: understand tokens and costs, design and test prompts, get validated JSON output, handle errors and safety risks, and finish with a guided document assistant project.
Level
01
Modules
05
Lessons
22
Introduction
Downloadable Assets and Resources
What a Language Model Does
Tokens, Context Windows and Cost
Choosing and Comparing Models
Calling a Model API Safely
Assessment
Calling a language model takes one line of code. Building something reliable on top of it takes engineering. This course teaches the foundations that every AI application shares: how a model behaves, how to design prompts, how to get structured and validated outputs, how to keep the cost and the risks under control, and how to test it, using the practical mindset of a software engineer and not the hype.
You do not have to pay for a model API to learn this. The course ships an offline stand-in model that behaves like a small language model, so every code output printed in the lessons is reproducible on your machine, deterministic and free. Real provider calls are shown in code blocks that you can run with your own key once you want to. Python and a text editor are enough.
Each lesson starts from a realistic engineering situation, then explains the concept with a precise definition, a diagram and the trade-offs, followed by a code demo with real output, exercises with hints and a short summary that leads to the next lesson.
The last module builds a Document Assistant that reads support emails, extracts and summarizes them into validated structured data and protects itself against injection and privacy problems. You get a template repository, a worked solution repository (zip files) and a set of test data: a file of support emails and a prompt lab file with examples.
Lessons on how models work, prompt design, structured outputs and safe use, an offline stand-in model so nothing needs an API key, support email data and a prompt lab file, a guided document assistant project with template and solution repositories, and a quiz for every module.
AI applications are being built everywhere, and the difference between a demo and a product is engineering. This course teaches you that difference from the first lesson, with a free offline model so you can start today with no account and no bill. If you can write some Python and want to build AI features you can rely on, enroll now and take the first step on the AI Engineering path.
SkillEnsure certificates reflect hands-on practice and real-world skills, not just watching videos or reaching the end of a course.
Learn moreI’ve worked in my field for years, but never had a structured way to prove my skills. They gave me a credible, verifiable certification that reflects my real ability.
Many certifications focus on course completion. SkillEnsure focuses on demonstrating understanding against defined competency criteria, which made the credential feel more valuable to me.
It was a great opportunity to strengthen my understanding of how AI is being applied in today’s technology and business environment, and I’m looking forward to continuing to build on these skills.
SkillEnsure helps link up what someone can actually do with formal recognition. That gap has always been a problem, and this finally takes care of it.
