From Full-Stack to AI Engineer: The Production Handbook
The shift to AI Engineering isn't about training models — it's about systems engineering.
Most developers are stuck in tutorial hell, building "Hello World" wrappers around the OpenAI API. They think the barrier is a PhD in Mathematics. That is false. This guide bridges the gap between standard Full-Stack development and the new reality of Generative AI, showing you exactly how to pivot without going back to university.
Inside this 21,000-word field manual:
Move past toy demos and build five production-grade architectures. Learn the specific engineering patterns for RAG, Autonomous Agents with function calling, and multi-modal applications. We focus on the "GenAI Stack": Vector Databases, Embeddings, Context Window management, and Token Economics.
What you'll build:
- Architect production-grade RAG systems using proper chunking strategies and vector storage
- Build autonomous AI Agents that utilize Function Calling to control external tools
- Implement Context Window memory management and streaming responses for optimized UX
- Integrate AI features into existing "brownfield" legacy applications
- Master the GenAI Tech Stack (Vector DBs, Orchestration, Observability)
- Calculate and optimize Token Economics to control your cloud budget
- Execute a specific 60-Day Build Plan to transition your portfolio
- Differentiate between ML Engineering (Training) and AI Engineering (Inference)
Who this is for:
Senior Developers, Full-Stack Engineers, and Backend devs who know how to code but don't know how to architect an AI system that doesn't hallucinate or bankrupt you.
By an instructor with 113K+ YouTube subscribers and a background leading tech teams at Fortune 500 companies.
If you don't feel more confident in your AI architecture skills after reading the first 3 chapters, 100% refund. Less than the cost of one month of ChatGPT Plus.
