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Video course · 15 chapters · 70 min · certificate

Job-Ready AI Product Management Bootcamp | Product Space

Become job-ready as an AI product manager: the mindset shift, probabilistic versus deterministic products, AI PM roles and stack, responsible AI, and how LLMs work from tokens and embeddings to attention and sampling.

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What you'll learn

  • Adopt the AI product management mindset
  • Distinguish probabilistic and deterministic systems
  • Compare AI-native and AI-feature PM roles
  • Map the AI stack a PM needs to know
  • Apply responsible AI guardrails
  • Explain tokenization, embeddings, attention and sampling

Chapters

15 chapters · 69:43
  1. 4:08 01Intro Members Welcome A bootcamp for PMs and aspiring PMs moving into AI product management.
  2. 2:21 02Overview Members Agenda What the two sessions cover and how they're structured.
  3. 2:36 03Mindset Members The AI PM mindset A PM's job is still solving user problems aligned with business goals; AI changes the how.
  4. 3:52 04Concepts Members Probabilistic versus deterministic AI systems are probabilistic; PMs must choose between traditional and generative approaches per use case.
  5. 3:28 05Roles Members AI applications versus AI PM Using AI apps differs from managing AI products; trade-offs remain the PM's job.
  6. 4:44 06Careers Members AI-native versus AI-feature PMs AI-native PM roles need deep technical depth; AI-feature PMs add AI to existing products.
  7. 4:40 07Stack Members The AI stack The layers of the AI stack a PM should understand.
  8. 4:39 08Responsibility Members Responsible AI and guardrails Guardrails for high-volume AI products like voice agents making many calls a day.
  9. 4:38 09LLMs Members Tokenization What happens when you press enter: text is split into tokens.
  10. 4:38 10LLMs Members Embeddings Embeddings give tokens meaning as vector coordinates.
  11. 2:18 11LLMs Members Positional encoding Word order matters: positional encoding tells the model where each token sits.
  12. 6:52 12LLMs Members Self-attention Self-attention, from the 2017 'Attention Is All You Need' paper, lets words take context from each other.
  13. 7:53 13LLMs Members Probability and temperature Softmax turns scores into probabilities; temperature controls randomness.
  14. 5:42 14LLMs Members Top-p and top-k sampling Top-k and top-p limit which tokens the model can choose from.
  15. 7:14 15Wrap-up Members Q&A and wrap-up Clarifying attention math (Q, K, V) and closing the session.

Jobs this course suits

Our AI checked this course against 500 jobs; these get the most out of it. Each job links to its learning path.

Product ManagersProduct OwnersChief Product Officers (CPOs)Innovation StrategistsCDOs (Chief Digital Officers)Product AnalystsTechnology ManagersProject ManagersChief Digital Officers (CDOs)IT Project Managers