Expert

Expert Track

For AI practitioners

You understand AI fundamentals. Dive into transformer internals, RAG pipelines, agent orchestration, fine-tuning, and production deployment.

Your progress 0 / 6 lessons

Understand the architecture powering every major LLM. Self-attention, positional encoding, multi-head attention, and why transformers won.

System prompts, structured output, JSON mode, prompt injection defence, and meta-prompting techniques for production AI systems.

From chunking strategies and embedding models to vector databases, hybrid search, re-ranking, and evaluation — the complete RAG engineering guide.

Build autonomous AI agents with the ReAct pattern, tool schemas, memory management, multi-agent systems, and safety guardrails.

When to fine-tune vs prompt vs RAG. LoRA, QLoRA, RLHF, DPO, dataset preparation, and evaluation — the complete guide to model customisation.

Scaling, monitoring, cost optimisation, latency reduction, caching, observability, and fallback strategies for production AI systems.

Other tracks