For AI practitioners
You understand AI fundamentals. Dive into transformer internals, RAG pipelines, agent orchestration, fine-tuning, and production deployment.
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 â 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 â the complete guide to model customisation.
Scaling, monitoring, cost optimisation, latency reduction, caching, observability, and fallback strategies for production AI systems.