Preface
Someone once said “Everyone uses technology, but as Engineers we have the ability to create technology.” While everyone around you might be using ChatGPT or a similar AI tool, only a few know how the underlying systems work — and only a handful know how to build their own GenAI apps.
AI engineering is a relatively new discipline that is rapidly evolving. As frontier AI model capabilities continually improve, previously successful techniques get re-evaluated and newer improved techniques get introduced. Along with models, there are new specs such as MCP v2, WebMCP, and others that evolve — all with a single goal of getting the best results from these AI models in the most cost effective manner. This is an ongoing iterative process resulting in a learning gap, and it may feel exhausting just trying to keep up with the pace.
AI is moving fast — If you haven’t tried AI in the last few months, what exists today would be unrecognizable to you.
This course attempts to help bridge that gap.
AI engineering enables safe, scalable deployment
Scaling GenAI programs demands robust engineering. This requires tools and frameworks for building, governing and customizing GenAI-powered applications. These solutions reduce hallucinations, mitigate disinformation and ensure regulatory compliance — while supporting broader organizational strategy.
A Unique Opportunity
No longer is AI the domain of only PhDs. Today, with open-source models, powerful APIs, and cloud platforms, building AI-powered applications is much more accessible to web developers.
Learning AI application development is a great way to transform from a frontend engineer to a next-generation solutions architect or full-stack engineer. The convergence of web expertise, modern AI tooling, and surging demand for intelligent digital experiences creates a landscape rich with opportunity.
- Building Agentic Systems
- Forward Deployed Engineering
- AI Transformation: Automation and Intelligence
“I use Agents to write all my code, do I still need to learn this?”
Great question — Engineers who use AI tools such as Claude Code, Cursor, or GitHub Copilot to write code are “AI-assisted Engineers.” This course teaches the skills to understand how such tools are built and how you can build everything that surrounds the AI model. You’re encouraged to write code with any AI-assisted coding tool of your choice.
You will learn to build GenAI powered applications and agentic systems that effectively deliver results for your end-users with rich UX through iterative development using the AI flywheel.
So while it’s easy to vibe code a chat assistant AI app, it’s much harder to build a chat assistant that always answers end-user questions in the expected manner.
AI Won’t Replace Humans — But Humans With AI Will Replace Humans Without AI
Learning to build GenAI powered applications puts you ahead of other Software Engineers.
Building a Mental Model is important
The most striking aspect of Naur’s perspective is his assertion that these mental models cannot be fully captured in documentation, comments, or even the code itself.
We believe that building is one of the best ways to learn — through the entire course, you will build 4 different projects and learn key concepts of GenAI development required to ship production-grade applications.
- Chatbot — Q&A
- Agentic Vibe Coding tool
- Knowledge base + Document Agent
- Creative Workflow automation tool
What's your primary motivation for learning AI engineering?
Check your understanding
What distinguishes an 'AI Engineer' from an 'AI-assisted Engineer'?
Which career paths does the lesson identify as opportunities for web developers learning AI? (Select all that apply)
According to Peter Naur, what is the essence of programming?