AI Training

Focuses on demystifying artificial intelligence, overcoming the initial learning curve, and establishing practical daily utility. Participants learn core concepts — such as how large language models function — alongside hands-on skills like prompt engineering, tool integration, and digital safety. No prior coding or technical background required.

1. Prompting 101: From Natural Language to Precise Results
2. Demystifying LLMs: How AI Actually “Thinks”
3. AI for Everyday Productivity: Automating Email, Summaries, and Search
4. Visual AI Foundations: Generating and Editing Media Responsibly
5. Navigating AI Bias, Privacy, and Hallucinations
6. Choosing Your AI Stack: Comparing ChatGPT, Claude, and Gemini
7. AI in the Workplace: Enhancing Work Without Replacing Workers
8. Voice Assistants and Agents: The Future of Human-Computer Interaction
9. Data Hygiene for Non-Engineers: Preparing Data for AI Tools
10. Creative Collaboration: Writing and Brainstorming with LLMs
11. Understanding Local AI: Running Open Models on Your Laptop
12. Ethics & Safety: A Beginner’s Guide to Responsible AI Use

Targeted at professionals, developers, and power users looking to integrate AI into existing workflows. Sessions move beyond simple interface usage into automation, fine-tuning, system design, retrieval-augmented generation (RAG), and evaluation metrics. Attendees learn how to build reliable, scalable solutions using modern APIs and low-code/no-code platforms.

1. Building Effective RAG Pipelines: Retrieval Beyond Vector Search
2. Fine-Tuning vs. In-Context Learning: Tailoring Models to Domain Data
3. Designing Autonomous AI Agents with Tool-Use Capabilities
4. Evaluating LLM Outputs: Benchmarks, LLM-as-a-Judge, and Metrics
5. Where Did My Job Just Go?
6. API Integration Patterns: Structuring Reliable JSON & Function Calling
7. Structured Output Parsing & Guardrails for Production
8. Multimodal Applications: Integrating Vision and Audio APIs
9. Local Model Deployment: Quantization, Ollama, and vLLM
10. Prompt Injection & Security Vulnerabilities in AI Apps
11. Workflow Automation: Connecting LLMs with Make, Zapier, and Python
12. Cost Optimisation & Token Budgeting for Scale

Designed for senior technical leaders, AI researchers, and principal engineers. Workshops tackle bleeding-edge research, deep model mechanics, custom architecture design, optimisation techniques, and high-level alignment or governance frameworks. Discussions focus on scalable infrastructure, theoretical limits, and novel AI paradigms.

1. Ethics & Safety: Advanced Risk and Safety in AI
2. Robotics and AI — Where Are We Heading?
3. Distributed Training Infrastructure: Scaling Across Multi-GPU Clusters
4. Post-Training Alignment: RLHF, DPO, and Preference Optimisation
5. State-Space Models vs. Transformers: Analysing Next-Gen Architectures
6. Speculative Decoding and Inference Acceleration Techniques
7. Mechanistic Interpretability: Peeking Inside the Black Box
8. Building Fault-Tolerant Multi-Agent Orchestration Frameworks
9. Hardware-Aware AI: Optimising Kernels with CUDA and Triton
10. Enterprise Governance, Red Teaming, and AI Risk Mitigation
11. Reasoning Models & Test-Time Compute Strategies
12. Frontier AI Safety: Technical Approaches to Alignment and Control