• The “big three” keys to a successful AI application

• What a full end-to-end AI project looks like

• How LLMs process instructions, “think” and access training data

• Getting consistent, structured data from AI

• Making AI responses directly usable in code

• Exercise: Build a recipe app that demonstrates structured outputs

• The power of RAG to differentiate your AI model

• Using vector databases to drive meaning and context

• Preparing documents for use in your RAG system

• Using observability to track everything happening in your AI app

• Verifying outputs with trained datasets and subject matter experts

• How safety systems prevent AI from going off the rails

• Breaking complex tasks into manageable pieces

• Using AI to improve code review and security processes

• Advanced features of the Cursor AI coding tool

• Implementing human-in-the-loop review systems

• Ethical considerations in AI development

• Future-proofing your engineering career

This isn't busy work. You'll build a working AI application that solves an actual problem. By the end, you'll have:

• A working AI application you built yourself

• Practical experience with the entire AI development pipeline

• A certificate that actually means something because it's backed by what you built