Learn product management in this order: product basics, discovery and user research, prioritisation, writing a spec, agile delivery, metrics and experiments, then AI product skills such as evaluation sets and fallbacks. Build artefacts, not just knowledge.
- 1Basicswhat PMs own
- 2Discoveryresearch, interviews
- 3PrioritiseRICE, trade-offs
- 4SpecifyPRDs, user stories
- 5Deliveragile, launches
- 6Measuremetrics, experiments
- 7AI productsevals, fallbacks
Learning path: free resources in order
| Stage | Free resource | Industry tool you will meet |
|---|---|---|
| 1. Basics and mindset | SVPG articles | Notion, Confluence |
| 2. Discovery and research | Google PAIR People + AI Guidebook | Interview notes, survey tools, session recordings |
| 3. Prioritisation and specs | Atlassian: product requirements | Jira, Linear, Confluence |
| 4. Agile delivery | The Scrum Guide | Jira, Linear |
| 5. Metrics and experiments | Amplitude North Star Playbook · Trustworthy Online Controlled Experiments | Amplitude, Mixpanel, SQL, experimentation platforms |
| 6. Design tools | Figma help centre | Figma |
| 7. AI product skills | Google PAIR Guidebook · Hugging Face LLM course | LLM APIs, evaluation sets, prompt tooling |
A realistic 12 to 24 week plan
| Weeks | Focus | Output you can show |
|---|---|---|
| Weeks 1 to 3 | Basics and discovery | Five user interviews and a one-page problem statement |
| Weeks 4 to 6 | Prioritisation and specs | A RICE-ranked backlog and one PRD |
| Weeks 7 to 9 | Metrics and experiments | A metrics tree and an experiment plan with sample size |
| Weeks 10 to 12 | AI product skills | An AI feature PRD with a 30-case eval pack |
Roles in the industry
| Role | What the work looks like | Core tools |
|---|---|---|
| Product manager | Own the problem, priorities and outcomes for a product area | Jira/Linear, Figma, analytics, SQL |
| Associate / junior PM | Write specs, run research, track metrics for a feature | Docs, analytics, spreadsheets |
| AI product manager | Ship features built on ML or LLMs; own evaluation and trust | Evaluation sets, LLM APIs, analytics |
| Product analyst | Funnels, retention and experiments that guide decisions | SQL, Python, BI tools |
Prioritisation methods to know
| Method | Idea | Use it when |
|---|---|---|
| Impact vs effort | Plot ideas on two axes and start top-left | You need a quick shared view |
| RICE | Score Reach x Impact x Confidence, divided by Effort | You have data on reach and want a ranked list |
| MoSCoW | Must, Should, Could, Won't | You are scoping a fixed release |
| Kano | Separate basic needs from delighters | You are deciding what makes a feature stand out |
Mistakes to avoid
- Writing a solution as the problem ("add a dashboard") instead of the user need.
- Choosing a vanity metric (sign-ups) over one that shows value delivered.
- Shipping an AI feature without a fallback and an evaluation set.
- Treating the roadmap as a promise instead of a set of bets.
Build as you learn
PM portfolio projects
Teardowns, funnel audits, AI feature specs and experiments.
Data analytics projects
SQL, cohorts, A/B tests and dashboards on real datasets.
Free tools for this field
Free AI tools for builders
Run, build and test AI projects without paying.
Free marketing tools
Measure, email, schedule and design with no budget.
Frequently asked questions
How do I become a product manager with no experience?
Build a product portfolio: a teardown, a funnel audit from public data and an AI feature spec. Move in from analytics, engineering, design or operations where you can.
Do product managers need to code?
No, but you need to understand how software, APIs and data work, and SQL helps you answer your own questions.
What is an AI product manager?
A PM who ships features built on models or LLMs and owns their evaluation, fallbacks, cost and trust.
What do PM interviews test?
Product sense, metrics, execution, strategy and behavioural stories. Practise structured answers on 15 to 20 cases.
Learn this with mentors
Product management programmes with admissions open: live classes, mentors and project work.
