Zero knowledge → degree → professional → expert

AI Builder: GPTs, Bots & Agents

Build custom GPTs, chatbots, agents and RAG systems — plus AI auditing and monitoring

40 structured topics, each reinforced with plain-language teaching, memory hooks, hands-on practice, retrieval, teach-back, speed recall, professional transfer and spaced review.

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How mastery works

1. Five-year-old simple

Every new term is explained without assumed knowledge and linked to a familiar picture.

2. Do it

Guided labs, typing, diagrams, configurations, queries or professional artefacts turn words into usable skill.

3. Retrieve it

No-peeking recall, teach-back and 60-second checks force the brain to retrieve instead of recognise.

4. Use it professionally

Failure modes, security, evidence, capstones and spaced repetition build degree and workplace fluency.

Course topics

  1. What Is AI, Really? — No-jargon explanation
  2. AI vs ML vs Deep Learning — Clearing up the buzzwords
  3. How LLMs Work — Tokens, prediction, context
  4. Prompting Basics — Asking so the model understands
  5. Prompt Patterns — Role, few-shot, chain-of-thought
  6. System Prompts — Setting the rules of behaviour
  7. Build Your First Custom GPT — Instructions, knowledge, tone
  8. Give a GPT Knowledge — Uploading and structuring files
  9. GPT Actions & Tools — Letting your AI use APIs
  10. Designing a Chatbot — Conversation flows that work
  11. Deploy a Chatbot — Website widget and messaging apps
  12. Calling an AI API — Requests, keys, responses
  13. Streaming Responses — Typing-effect answers
  14. Embeddings Explained — Turning meaning into numbers
  15. Vector Databases — Storing and searching meaning
  16. RAG: Retrieval Augmented Generation — Grounding AI in your data
  17. Chunking & Context Windows — Feeding documents correctly
  18. AI Agents — Models that take actions
  19. Tool / Function Calling — Structured outputs and tools
  20. Multi-Agent Systems — Teams of specialised agents
  21. AI Workflow Automation — Zapier/n8n style pipelines
  22. Fine-Tuning — When to train vs prompt
  23. Evaluating AI Outputs — Golden sets and scoring
  24. Hallucinations — Why they happen and how to reduce them
  25. AI Auditing — Auditing models for risk and fairness
  26. AI Monitoring in Production — Drift, latency, cost, quality
  27. Guardrails & Safety — Blocking harmful or off-topic output
  28. Prompt Injection Defence — Securing AI applications
  29. Bias & Fairness Testing — Measuring disparate impact
  30. Explainability — SHAP, LIME, model cards
  31. AI Governance & EU AI Act — Policy, risk tiers, documentation
  32. Data Privacy for AI — GDPR, PII redaction, retention
  33. Cost & Token Optimisation — Cheaper, faster AI
  34. Image Generation — Diffusion models and prompting
  35. Speech & Voice AI — TTS, STT, voice assistants
  36. Vision Models — Understanding images
  37. Running Local Models — Ollama, quantisation, hardware
  38. MLOps Basics — Versioning, CI/CD for models
  39. Shipping an AI Product — UX, trust, feedback loops
  40. Capstone: Build an AI Assistant — End-to-end AI application

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