Curriculum
How the program is structured
Foundations
LLM engineering fundamentals — prompting, context design and working with model APIs.
Retrieval & Grounding
RAG architecture, vector search and retrieval quality evaluation.
Agents & Orchestration
Tool-calling patterns, multi-step planning and designing for graceful failure.
Evaluation & Production
AI evaluation methodology, deployment patterns and monitoring for AI systems.
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
Comfortable writing production code and reasoning about system design. No prior AI experience required.
By the end, you'll be able to
- Design and build LLM-powered features using retrieval-augmented generation.
- Build tool-using AI agents that handle multi-step tasks and realistic failure cases.
- Write evaluation suites that catch quality regressions before users do.
- Talk through the production tradeoffs — cost, latency, reliability — of AI systems in an interview.
Who this program is for
- Software engineers with production experience who want to move into AI Engineer roles.
- Engineers comfortable with system design and debugging complex systems.
- Not intended as a first introduction to programming or software engineering fundamentals.
Projects, assessment and verification
Practical Projects
- AI Research Agent
- Enterprise RAG Assistant
Assessment
Role-specific technical assessment covering LLM engineering, agent design and evaluation methodology.
Verification
Verified ITtoAI profile with assessed skills and reviewed project submissions.
Career Relevance
Maps directly to AI Engineer hiring requirements — tool-using agents, retrieval systems and evaluation rigor.
Completing this program does not guarantee employment, placement or salary outcomes. It builds practical capability and an evidence-based profile that demonstrates it.