Curriculum
How the program is structured
LLM API Integration
Working with model APIs, structured prompting and context management.
RAG for Applications
Building retrieval-augmented features grounded in real product data.
Production Deployment
Shipping AI-powered services with FastAPI, with attention to cost and latency.
Capstone & Verification
Build a practical project end-to-end and complete the role-specific technical assessment.
What you'll learn and who this is for
Full Skill List
Prerequisites
Working experience building and shipping APIs or backend services.
By the end, you'll be able to
- Integrate LLM APIs into existing backend services with proper error handling.
- Build a retrieval-augmented feature grounded in real, messy data.
- Deploy an AI-powered API with attention to cost, latency and reliability.
- Explain the tradeoffs behind a GenAI feature’s design in a technical interview.
Who this program is for
- Developers with working experience in Python or similar backend languages.
- Engineers who want to move into GenAI application development, not model research.
Projects, assessment and verification
Practical Projects
- Production AI API
- AI Document Intelligence
Assessment
Technical assessment covering LLM API integration, RAG architecture and production deployment.
Verification
Verified ITtoAI profile with assessed skills and reviewed project submissions.
Career Relevance
Reflects the day-to-day work of GenAI application teams — shipping AI features into real products.
Completing this program does not guarantee employment, placement or salary outcomes. It builds practical capability and an evidence-based profile that demonstrates it.