Technical depth for engineers, product managers and leaders who are building, deploying or governing AI systems. NZ and AU context throughout.
Transformer architecture, tokenisation, training objectives, scaling laws and emergent behaviour.
Chain-of-thought, few-shot patterns, system prompts, structured outputs and prompt injection defence.
Vector databases, embeddings, chunking strategies, hybrid search and evaluation frameworks.
Agent loops, tool use, multi-agent orchestration, planning and reliability patterns.
AI risk frameworks, bias evaluation, model auditing, NZ ALGO charter and GDPR/Privacy Act considerations.
Model selection, cost optimisation, observability, latency trade-offs and production reliability.
Building an AI-first roadmap, evaluating vendor tools, managing transformation risk and measuring ROI.