AI Systems Engineering: From Token to RAG · MyLeoNes™ AI Academy

A university-level pathway to build, measure and defend an AI system with RAG, memory, local models and real guardrails.

Project outcome

Repository or runnable prototype

What is inside

1. Tokens and transformers
2. Training, fine-tuning and inference
3. SLMs, frontier models and open source
4. GPU, VRAM and serving
5. Retrieval: documents, chunks and embeddings
6. Reranking, citations and grounded answers
7. Memory and context
8. Orchestration and agents
9. Diffusion and multimodal systems
10. Evaluation, red teaming and capstone

Skills demonstrated

• Logic and Data
• AI Agents
• Testing and Improvement

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