-
How to review AI-generated code when you are still learning
A small booking-page exercise to help you question AI-generated code, test awkward inputs and leave useful review notes.
-
AI personas are not customer evidence: a practical research sorting exercise
Separate observations, interpretations and AI-generated suggestions before they become product decisions.
-
Stop reopening the same product decision: use a six-part decision log
Make recurring product discussions actionable with a clear question, decision owner, evidence and review trigger.
-
How to learn to code with AI without skipping the fundamentals
AI can produce a working page before you understand why it works. That can feel exciting, then frustrating when you want to change one small thing and have no idea where to start.…
-
AI literacy for women in tech: 7 skills to practise this week
You do not need to build an AI model to ask useful questions about one. If an AI tool is helping your team write, analyse, code or make recommendations, you need enough understanding…
-
From Developer to Product Owner: a practical transition plan
You enjoy building software, but you are becoming more interested in the decisions around it. Which problem deserves attention? What should happen first? How will anyone know whether the feature helped? Those questions…
-
How to write a tech CV that shows your impact, even without metrics
Your CV says you attended meetings, managed tickets and supported delivery. All of that may be true, but it leaves the reader guessing about what your contribution actually involved. Advice to “add numbers”…
-
How to measure whether an AI feature is actually helping users
People are opening your new AI assistant. They are submitting questions and trying different prompts. Your dashboard is busy, but one question remains: is the feature helping them finish the work they came…
-
How to use AI for backlog preparation without outsourcing priorities
Your feedback is spread across meeting notes, support requests and conversations. Before you can discuss priorities, someone has to organise it into something the team can understand. AI can help with that preparation.…
-
Sprint planning with realistic capacity: include the work nobody counts
The team leaves Sprint Planning with a full list of work. A week later, progress looks different from the forecast. Someone has planned leave, support requests have arrived and a review queue needs…
