Product Managers & Leaders

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1. What AI, ML, Deep Learning and LLMs Are

   Stage 0 · 3 h · 3 h total · lesson ready

   One map of the AI family, and how an LLM “predicts the next token”.

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2. Prompting & Using AI Well

   Stage 0 · 3 h · 6 h total · lesson ready

   Prompting techniques: role, context, examples, step-by-step thinking and structured output.

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3. Hallucination, Reliability & Limits

   Stage 0 · 2 h · 8 h total

   Why models confidently make things up, and how to check and reduce the risk.

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4. Pretraining, Data & Scaling Laws

   Stage 4 · 10 h · 18 h total

   Pre-training data pipelines, scaling laws, Chinchilla, μP, MTP and training stability.

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5. Reasoning Models: RLVR, GRPO & Test-Time Compute

   Stage 5 · 12 h · 30 h total

   Chain of thought, self-consistency, process rewards, GRPO/DAPO, verifiable rewards and the R1 recipe.

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6. Serving Engines

   Stage 6 · 8 h · 38 h total

   PagedAttention, continuous batching, prefix caching, speculative decoding, prefill/decode disaggregation and gateway routing.

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7. RAG Engineering & Evaluation

   Stage 8 · 8 h · 46 h total · lesson ready

   Chunking, query rewriting, HyDE, GraphRAG, citations and evaluation metrics.

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8. Agents: Tool Use, MCP, Planning & Memory

   Stage 8 · 12 h · 58 h total · lesson ready

   ReAct, function calling, MCP, reflection, memory, multi-agent systems, computer use and sandboxes.

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9. AI Engineering: Evals, Cost & Shipping

   Stage 8 · 8 h · 66 h total

   Your own eval sets, LLM judges, observability, model gateways, cost control and product design.

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10. Evaluation & Benchmarks

    Stage 9 · 6 h · 72 h total · lesson ready

    Major benchmarks, arenas, data contamination and the limits of capability evaluation.

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11. Security: Jailbreaks, Injection & Privacy

    Stage 9 · 6 h · 78 h total · lesson ready

    Adversarial examples, GCG, prompt injection, red-teaming, watermarking, differential privacy and backdoors.

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12. Frontier Risk, Scalable Oversight & Governance

    Stage 9 · 6 h · 84 h total

    Weak-to-strong generalisation, debate, Constitutional AI, risk frameworks and regulation (EU AI Act, NIST AI RMF).

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• 1Everyday Users · No code: in 2–3 weeks, learn to understand and use AI well and spot hallucinations and risks.40h
• 2Application Developers · Python is enough: in 3 months, ship a production-ready RAG / agent app you can evaluate and deploy.126h
• 3Algorithm Researchers · From maths to frontier papers: in 6–12 months, read and reproduce papers on LLMs, alignment and reasoning models.257h
• 4Domain Scientists · With a background in your own field, learn to do research with ML and LLMs — linked with the “AI × Disciplines” section.143h